Data processing and publishing method, device and storage medium

Abstract

The embodiment of the present application provides a data processing and publishing method, device and storage medium. In the embodiment of the present application, an ordered original data set corresponding to the original data to be desensitized and an ordered desensitized data set corresponding to the ordered original data set are provided; in the desensitization process, the target original data corresponding to the original data to be desensitized is searched in the ordered original data set, and then based on the set seed key, the target desensitized data corresponding to the target original data is randomly sampled in the ordered desensitized data set corresponding to the ordered original data set as the desensitization result of the original data to be desensitized. In this scheme, the target desensitized data randomly sampled based on the seed key can retain the orderliness between the original data to be desensitized, thereby realizing order-preserving desensitization, which enables the data user to perform subsequent processing based on the orderliness retained by the desensitized data, and on the basis of improving data security, it can also ensure the availability of data.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a data processing and publishing method, device and storage medium. Background Art

[0002] With the development of technologies such as the Internet and cloud computing, data sources can either upload their data to the cloud for storage, or open their data to external users for reference or information. Whether uploading data to the cloud for storage or opening data to external users, the data may contain some sensitive data. To avoid leakage of sensitive data, it is necessary to desensitize the data.

[0003] There are various data desensitization methods in the prior art, for example, generating a random seed for each sensitive data and performing a hash mapping replacement on the sensitive data; another example, re-randomly distributing the sensitive data to confuse the original value and the connection with other fields; another example, for numerical data, first calculating their mean, and then making the desensitized values ​​randomly distributed around the mean, so as to keep the sum of the data unchanged. These data desensitization methods can ensure the security of the data, but they destroy some of the original characteristics between the data and cannot guarantee the availability of the data. Summary of the invention

[0004] Multiple aspects of the present application provide a data processing and publishing method, device and storage medium to improve the security and availability of data.

[0005] An embodiment of the present application provides a data processing method, which includes: obtaining the original data to be desensitized from the data source end according to a call request initiated by the data source end; searching for target original data corresponding to the original data to be desensitized in an ordered original data set corresponding to the original data to be desensitized; and based on a set seed key, randomly sampling target desensitized data corresponding to the target original data in an ordered desensitized data set corresponding to the ordered original data set; returning the target desensitized data to the data source end so that the data source end can output the target desensitized data; wherein the target desensitized data randomly sampled based on the seed key retains the orderliness between the original data to be desensitized.

[0006] The embodiment of the present application also provides a data publishing method, comprising: according to a call request initiated by an e-commerce platform, obtaining original public domain data that the e-commerce platform needs to publish to merchants; searching for target public domain data corresponding to the original public domain data in an ordered public domain data set corresponding to the original public domain data; based on a set seed key, randomly sampling target desensitized data corresponding to the target public domain data in an ordered desensitized data set corresponding to the ordered public domain data set; returning the target desensitized data to the e-commerce platform, so that the e-commerce platform can use the target desensitized data to replace the original public domain data and then publish it to merchants; wherein the target desensitized data randomly sampled based on the seed key retains the orderliness between the original public domain data.

[0007] An embodiment of the present application also provides a data processing method, which includes: obtaining original data to be desensitized generated during the operation process; searching for target original data corresponding to the original data to be desensitized in an ordered original data set corresponding to the original data to be desensitized; and based on a set seed key, randomly sampling target desensitized data corresponding to the target original data in an ordered desensitized data set corresponding to the ordered original data set; and outputting the target desensitized data; wherein the target desensitized data randomly sampled based on the seed key retains the orderliness between the original data to be desensitized.

[0008] The embodiment of the present application also provides a data publishing method, including: an e-commerce platform obtains original public domain data that needs to be released to merchants; in an ordered public domain data set corresponding to the original public domain data, searching for target public domain data corresponding to the original public domain data; based on a set seed key, randomly sampling target desensitized data corresponding to the target public domain data in an ordered desensitized data set corresponding to the ordered public domain data set; and replacing the original public domain data with the target desensitized data and then publishing it to merchants; wherein the target desensitized data randomly sampled based on the seed key retains the orderliness between the original public domain data.

[0009] An embodiment of the present application also provides a data processing device, including: a memory, a processor and a communication component; the memory is used to store a computer program; the processor is coupled to the memory and is used to execute the computer program, so as to: obtain the original data to be desensitized from the data source end through the communication component according to a call request initiated by the data source end; search for target original data corresponding to the original data to be desensitized in the ordered original data set corresponding to the original data to be desensitized; and based on a set seed key, randomly sample target desensitized data corresponding to the target original data in the ordered desensitized data set corresponding to the ordered original data set; return the target desensitized data to the data source end, so that the data source end can output the target desensitized data; wherein the target desensitized data randomly sampled based on the seed key retains the orderliness between the original data to be desensitized.

[0010] An embodiment of the present application also provides a data processing device, including: a memory and a processor; the memory is used to store a computer program; the processor is coupled to the memory and is used to execute the computer program, so as to: obtain the original data to be desensitized generated by the data processing device during operation; search for target original data corresponding to the original data to be desensitized in an ordered original data set corresponding to the original data to be desensitized; and based on a set seed key, randomly sample target desensitized data corresponding to the target original data in an ordered desensitized data set corresponding to the ordered original data set; output the target desensitized data; wherein the target desensitized data randomly sampled based on the seed key retains the orderliness between the original data to be desensitized.

[0011] The embodiment of the present application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor implements the steps in the data processing method provided in the embodiment of the present application.

[0012] The embodiment of the present application provides an order-preserving desensitization scheme, in which an ordered original data set corresponding to the original data to be desensitized and an ordered desensitized data set corresponding to the ordered original data set are provided; in the desensitization process, the target original data corresponding to the original data to be desensitized is searched in the ordered original data set, and then based on the set seed key, the target desensitized data corresponding to the target original data is randomly sampled in the ordered desensitized data set corresponding to the ordered original data set as the desensitization result of the original data to be desensitized. In this scheme, the target desensitized data randomly sampled based on the seed key can retain the orderliness between the original data to be desensitized, thereby achieving order-preserving desensitization, which enables the data user to perform subsequent processing based on the orderliness retained by the desensitized data, and on the basis of improving data security, the availability of the data can also be guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0014] Figure 1a A schematic diagram of the structure of a data processing system provided for an exemplary embodiment of the present application;

[0015] Figure 1b A schematic diagram of the structure of another data processing system provided for an exemplary embodiment of the present application;

[0016] Figure 2a A flowchart of a data processing method provided for an exemplary embodiment of the present application;

[0017] Figure 2b A flowchart of a data publishing method provided for an exemplary embodiment of the present application;

[0018] Figure 3a A flowchart of another data processing method provided for an exemplary embodiment of the present application;

[0019] Figure 3b A flowchart of another data publishing method provided for an exemplary embodiment of the present application;

[0020] Figure 4 A structural schematic diagram of a data processing device provided for an exemplary embodiment of the present application;

[0021] Figure 5 A schematic structural diagram of another data processing device provided for an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0023] In view of the technical problem that the existing data desensitization method can ensure the security of data, but destroys some original characteristics between data and cannot ensure the availability of data, the embodiment of the present application provides an order-preserving desensitization scheme, in which an ordered original data set corresponding to the original data to be desensitized and an ordered desensitized data set corresponding to the ordered original data set are provided; in the desensitization process, the target original data corresponding to the original data to be desensitized is searched in the ordered original data set, and then based on the set seed key, the target desensitized data corresponding to the target original data is randomly sampled in the ordered desensitized data set corresponding to the ordered original data set as the desensitization result of the original data to be desensitized. In this scheme, the target desensitized data randomly sampled based on the seed key can retain the order between the original data to be desensitized, thereby realizing order-preserving desensitization, which enables the data user to perform subsequent processing based on the order retained by the desensitized data, and on the basis of improving data security, the availability of the data can also be guaranteed.

[0024] The technical solutions provided by various embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0025] Figure 1a The following is a schematic diagram of a data processing system provided by an exemplary embodiment of the present application. Figure 1aAs shown, the system 100 includes: a data source end 101 and a data processing end 102 .

[0026] In this embodiment, the data source end 101 may be an end that has a data desensitization requirement and can provide the original data to be desensitized. In terms of implementation, the data source end 101 may be any application, application system, functional module in the application system, client, hardware device (such as a server, terminal device) or hardware chip (such as a CPU, GPU or FPGA) that can generate the original data to be desensitized. In this embodiment, the deployment implementation form of the data source end 101 is not limited.

[0027] In this embodiment, the data processing end 102 refers to an end that can perform desensitization processing on the original data to be desensitized. In terms of implementation form, the data processing end 102 can be implemented as an application, plug-in, patch, service, instance, software-form functional module, library function with data desensitization function, or it can also be implemented as a hardware device (such as a server or terminal device), hardware chip (such as CPU, GPU or FPGA), virtual machine (Virtual Machine, VM) or container with data desensitization function.

[0028] In this embodiment, the deployment implementation form of the data processing end 102 is not limited. In terms of deployment implementation, the data processing end 102 can be centrally deployed, for example, the data processing end 102 can be centrally deployed on a physical machine (such as a server) or on a virtual machine. Figure 1a As shown, the deployment on one server is used as an example. Alternatively, in terms of deployment, the data processing end 102 may also be deployed in a distributed manner, for example, the data processing end 102 may be deployed in a distributed manner on multiple physical machines, or may be deployed in a distributed manner on multiple virtual machines. Figure 1b As shown, the distributed deployment of the data processing terminal 102 on multiple servers is used as an example for illustration. Regardless of whether the data processing terminal 102 is deployed in a distributed manner or in a centralized manner, one thread or multiple threads can be started in each physical machine or virtual machine where the data processing terminal 102 is deployed, and these threads implement the data processing function of the data processing terminal 102, mainly referring to the data desensitization function.

[0029] In this embodiment, the data source end 101 is responsible for generating raw data. Depending on the application scenario, the raw data generated by the data source end 101 will be different. For example, if the data source end 101 is a shopping application, the raw data generated by it includes but is not limited to: sales of goods in various dimensions, sales volume of goods, number of product reviews in various dimensions, and quantity of goods, types of goods, inventory, etc. under various categories. For another example, if the data source end 101 is a game application, the raw data generated by it includes but is not limited to: the number of game players, age groups, gender ratios, sales volume and sales of various game props, etc.

[0030] In addition to generating raw data, the data source end 101 also has the need to output raw data. For example, the data source end 101 may need to take advantage of the advantages of cloud storage to upload the raw data it generates to the cloud for storage. For another example, the data source end 101 may need to disclose the raw data it generates to the user for various reasons such as development, attracting users or information disclosure. However, considering that there may be some sensitive information in the raw data, or data that is not easy to disclose directly to the public, in order to avoid the leakage of sensitive information in the data, the data source end 101 can call the data processing end 102 to perform order-preserving desensitization (OPD) on the desensitized raw data, and output the order-preserving desensitized data. In this way, the orderliness between the data can be guaranteed, the data availability can be guaranteed as much as possible, and the leakage of sensitive information can be avoided to ensure data security.

[0031] Specifically, when the data source end 101 needs to desensitize the original data, it can initiate a call request to the data processing end 102, calling the data processing end 102 to perform desensitization processing on the original data to be desensitized. Accordingly, the data processing end 102 can receive the call request initiated by the data source end 101, and obtain the original data to be desensitized from the data source end 101 according to the call request initiated by the data source end 101; then, perform order-preserving desensitization processing on the original data to be desensitized.

[0032] In this embodiment, the call request initiated by the data source end 101 may directly carry the original data to be desensitized, based on which, the data processing end 102 may directly obtain the original data to be desensitized from the call request initiated by the data source end 101. Alternatively, the call request initiated by the data source end 101 may carry data parameters pointing to the original data to be desensitized. For example, the data parameter may be the storage location information of the original data to be desensitized, and the data processing end 102 may obtain the original data to be desensitized from the storage location information. For another example, the data parameter may be attribute information that can uniquely identify the original data to be desensitized, for example, the attribute information is the category attribute and range attribute of the original data to be desensitized, such as the sales of commodity A in the last three months, then the data processing end 102 can obtain the original data corresponding to the attribute information from various types of original data generated by the data source end 101 as the original data to be desensitized based on the attribute information of the original data to be desensitized.

[0033] After obtaining the original data to be desensitized, the data processing end 102 starts to perform order-preserving desensitization processing on the original data to be desensitized. Specifically, the data processing end 102 can search for the target original data corresponding to the original data to be desensitized in the ordered original data set corresponding to the original data to be desensitized; then, based on the set seed key, randomly sample the target desensitized data corresponding to the target original data in the ordered desensitized data set corresponding to the ordered original data set. Among them, the target desensitized data is the desensitization result of the original data to be desensitized. After obtaining the target desensitized data, the data processing end 102 returns the target desensitized data to the data source end 101, so that the data source end 101 can output the target desensitized data. After initiating the call request, the data source end 101 can receive the target desensitized data returned by the data processing end 102, replace the original data to be desensitized with the target desensitized data, and output the target desensitized data. Among them, the target desensitized data randomly sampled based on the seed key can retain the order between the original data to be desensitized, thereby achieving order-preserving desensitization. This allows data users (such as users who see the target desensitized data) to perform subsequent processing based on the order retained by the desensitized data, such as comparing data size, sorting, and various data analyses. On the basis of improving data security, it also ensures data availability.

[0034] In this embodiment, for the convenience of description, the original data corresponding to the original data to be desensitized in the ordered original data set is referred to as the target original data. The correspondence between the target original data and the original data to be desensitized means that the target original data and the original data to be desensitized have a certain corresponding relationship, for example, the corresponding relationship can be but is not limited to: an equal or identical relationship, a direct proportional relationship, an inverse proportional relationship, an exponential relationship or a logarithmic relationship, etc.

[0035] In this embodiment, the ordered original data set may be a set of original data including the original data to be desensitized, or may be a set of original data that has a certain correspondence with the original data to be desensitized, in which the orderliness between the original data is retained. The orderliness between the original data refers to the sequence and / or size relationship between the original data. Taking the original data to be desensitized as numerical data as an example, the original data in the ordered original data set is also numerical data. For numerical data, orderliness refers to the numerical size relationship between numerical data, and the original data in the ordered original data set can be arranged in order from small to large or from large to small. In addition, based on the numerical data, the ordered original data set may be a set of numerical data including the numerical data to be desensitized, or may be a set of numerical data including numerical data that has an N-fold relationship with the numerical data to be desensitized; N is a positive integer.

[0036] In the present embodiment, the ordered desensitized data set is a collection of desensitized data. The orderliness between desensitized data refers to the order and / or size relationship between the desensitized data. Taking the original data to be desensitized as numerical data as an example, the desensitized data in the ordered desensitized data set is also numerical data. For numerical data, orderliness refers to the numerical size relationship between numerical data, so the desensitized data in the ordered desensitized data set can be arranged in order from small to large or from large to small. Among them, the ordered desensitized data set corresponds to the ordered original data set, and is responsible for providing corresponding desensitized data for the original data in the ordered original data set; in other words, the desensitized data of the original data in the ordered original data set comes from the ordered desensitized data set. However, in the present embodiment, the correspondence between the original data in the ordered original data set and the desensitized data in the ordered desensitized data set is not directly limited. The correspondence between the original data in the ordered original data set and the desensitized data in the ordered desensitized data set is based on the seed key and is obtained by random sampling, and the random sampling result can ensure that the desensitized data can retain the orderliness between the original data.

[0037] In this embodiment, the size relationship between the number of data contained in the ordered original data set and the number of data contained in the ordered desensitized data set is not limited. Among them, the number of data contained in the ordered original data set can be greater than the number of data contained in the ordered desensitized data set. For example, the ordered original data set contains 10,000 original data, such as numerical data 1-10,000, and the ordered desensitized data set contains 100 desensitized data, such as numerical data 200-300, that is, numerical data 1-10,000 can be mapped to numerical data 200-300 according to the size relationship to achieve order-preserving desensitization. In the case where the number of data contained in the ordered original data set can be greater than the number of data contained in the ordered desensitized data set, there are multiple original data mapped to the same desensitized data, that is, it is impossible to reverse the desensitized data to the original data, that is, it is irreversible, which can further ensure the security of the original data. Of course, the number of data contained in the ordered original data set can also be less than the number of data contained in the ordered desensitized data set; or, the number of data contained in the ordered original data set can also be equal to the number of data contained in the ordered desensitized data set, which is not limited. It should be noted that when the number of data contained in the ordered original data set is less than or equal to the number of data contained in the ordered desensitized data set, a reasonable random sampling algorithm can be used to map the original data in the ordered original data set to part of the desensitized data in the ordered desensitized data set while ensuring orderliness. The number of partial desensitized data is less than the number of original data in the ordered original data set, thereby achieving an irreversible effect and further ensuring the security of the original data.

[0038] In this embodiment, the set seed key is the random seed used for random sampling, that is, each random sampling is based on the same seed key. In the random sampling process, the seed key is used as the initial condition, and then a certain algorithm is used to iterate and generate the result of each random sampling. Optionally, the seed key can come from the key space K. The larger the key space K, the better. In this way, it is more difficult for an attacker to enumerate all the keys in the key space K, which is conducive to improving the security of the seed key, increasing the difficulty of cracking the desensitization process, and further ensuring data security. For example, the key space K is {0,1}^128, that is, the key in the key space K is a series of 128-bit bit strings composed of 0 and 1. In this embodiment, the random sampling process refers to the process of sampling desensitized data from an ordered desensitized data set. The random sampling process can be implemented by a random sampling algorithm, but the random sampling algorithm is not limited. For example, the random sampling algorithm can be but is not limited to: Gaussian sampling, Laplace sampling, uniform sampling, truncated sampling, folded sampling or resampling, etc. Among them, the Gaussian sampling algorithm refers to a sampling algorithm whose random sampling results have Gaussian distribution characteristics; the uniform sampling algorithm refers to a sampling algorithm whose random sampling results have uniform distribution characteristics.

[0039] In this embodiment, the target desensitized data randomly sampled based on the seed key retains the orderliness between the original data to be desensitized. Therefore, the application requirements originally based on the orderliness of the original data can be implemented based on the desensitized data, thereby maintaining the availability of the data.

[0040] In this embodiment, before using the ordered original data set, the ordered desensitized data set, and the set seed key, the process of obtaining the ordered original data set, the ordered desensitized data set, and the set seed key is also included. The embodiment of the present application does not limit the method of obtaining the ordered original data set, the ordered desensitized data set, and the set seed key, and the following examples are given:

[0041] In embodiment A1, the data source end 101 is responsible for providing the ordered original data set and the ordered desensitized data set, and for setting the seed key. Based on this, the data processing end 102 directly obtains the ordered original data set, the ordered desensitized data set and the set seed key from the data source end 101.

[0042] In embodiment A2, the data source end 101 is responsible for setting the seed key and providing the original data to be desensitized. Based on this, the data processing end 102 can directly obtain the set seed key from the data source end 101. In addition, the data processing end 102 can estimate the original data of the ordered original data set according to the maximum and minimum values ​​corresponding to the original data to be desensitized, so as to obtain the ordered original data set. For example, the maximum value of the original data to be desensitized can be used as the upper limit of the ordered original data set, and the minimum value of the original data to be desensitized can be used as the lower limit of the ordered original data set, and then the value X (such as 1) is used as the step length to divide the value between the upper limit and the lower limit of the ordered original data set to obtain the ordered original data set. In addition, an implementation method for obtaining an ordered desensitized data set can be to divide the maximum and minimum values ​​corresponding to the original data to be desensitized by a constant, such as 2, 3, 4, etc., to obtain the upper limit and lower limit of the ordered desensitized data set, and then the value Y (such as 1) is used as the step length to divide the value between the upper limit and the lower limit of the ordered original data set to obtain the ordered desensitized data set.

[0043] In implementation A3, the data processing end 102 can read the preset ordered original data set corresponding to the original data to be desensitized, the ordered desensitized data set corresponding to the ordered original data set, and the set seed key from the designated data storage space. The ordered original data set, the ordered desensitized data set, and the set seed key are pre-set.

[0044] In the embodiment of the present application, the method in which the data processing end 102 searches for the target original data corresponding to the original data to be desensitized in the ordered original data set is not limited. The following is an example:

[0045] In an optional embodiment B1, the target original data corresponding to the original data to be desensitized can be directly searched in sequence in the ordered original data set. For example, the target original data corresponding to the original data to be desensitized can be searched in sequence starting from the original data ranked first in the ordered original data set. Wherein, if the ordered original data set is sorted in the order of data size from small to large or in the order of data generation time from early to late, the original data ranked first is the smallest data or the earliest generated data; if the ordered original data set is sorted in the order of data size from large to small or in the order of data generation time from late to early, the original data ranked first is the largest data or the latest generated data. Wherein, on the basis of sequential search, a temporary original data will be read in sequence each time the search is performed, and the temporary original data will be compared with the original data to be desensitized; if the two do not correspond, the next search will be continued until the target original data corresponding to the original data to be desensitized is found. Accordingly, corresponding to each search, temporary desensitized data corresponding to the temporary original data will be randomly sampled in the desensitized data subset to be sampled this time based on the set seed key; if the temporary original data in this search does not correspond to the original data to be desensitized, then part of the desensitized data including the temporary desensitized data sampled this time is obtained from the desensitized data subset to be sampled this time to form the next desensitized data subset to be sampled; if the temporary original data in this search corresponds to the original data to be desensitized, that is, the temporary original data in this search is the target original data corresponding to the original data to be desensitized, then the temporary desensitized data sampled this time is the target desensitized data corresponding to the target original data. Among them, in the first sampling process, the desensitized data subset to be sampled is the entire ordered desensitized data set.

[0046] In another optional embodiment B2, a binary search method can be used to search for target original data corresponding to the original data to be desensitized in the ordered original data set. Accordingly, an implementation method of randomly sampling target desensitized data corresponding to the target original data in an ordered desensitized data set corresponding to the ordered original data set based on a set seed key includes: using a binary search method, based on a set seed key, randomly sampling target desensitized data corresponding to the target original data in the ordered desensitized data set.

[0047] Further optionally, an implementation method of using binary search to search for target original data corresponding to the original data to be desensitized in an ordered original data set includes: in each binary search process, comparing the original data to be desensitized with the intermediate original data in the original data interval to be searched this time; wherein, the original data interval to be searched in the first binary search process is an ordered original data set; if the original data to be desensitized is different from the intermediate original data, then half of the data interval including the intermediate original data in the original data interval to be searched this time is used as the original data interval to be searched next time to continue the next binary search, until the intermediate original data identical to the original data to be desensitized is found as the target original data.

[0048] Further optionally, a method of using half of the data interval including the intermediate original data in the original data interval to be searched this time as the original data interval to be searched next time to continue the next binary search until the intermediate original data identical to the original data to be desensitized is found as the target original data includes: if the original data to be desensitized is larger than the intermediate original data m_mid in the original data interval [m_min, m_max] to be searched this time, then using the closed interval [m_mid, m_max] as the original data interval [m_min, m_max] to be searched next time to continue the next binary search until the intermediate original data identical to the original data to be desensitized is found as the target original data; if the original data to be desensitized is smaller than the intermediate original data m_mid in the original data interval [m_min, m_max] to be searched this time, then using the closed interval [m_min, m_mid] as the original data interval [m_min, m_max] to be searched next time to continue the next binary search until the intermediate original data identical to the original data to be desensitized is found as the target original data. Among them, “[]” represents a closed interval, “m_min” represents the lower limit of the original data interval, “m_max” represents the upper limit of the original data interval, and “m_mid” represents the middle original data.

[0049] In this embodiment, the intermediate raw data m_mid may be data located in the middle of the raw data interval [m_min, m_max]. Alternatively, taking numerical data as an example, the intermediate raw data m_mid may also be the average value in the raw data interval [m_min, m_max], that is, the intermediate raw data m_mid = (m_min + m_max) / 2. Considering that decimals may appear when calculating the intermediate raw data, in order to facilitate calculation, the calculated intermediate raw data may be rounded down or rounded up, that is, the intermediate raw data or in, Indicates the floor symbol, Indicates the rounding symbol. Further, when determining the original data interval, in order to avoid the influence of the rounding operation, so that each original data to be desensitized can have corresponding desensitized data, in this embodiment, the original data interval is set as a closed interval.

[0050] In an optional embodiment, a binary search method is used to randomly sample target desensitized data corresponding to the target original data in an ordered desensitized data set based on a set seed key, including: in each binary search process, based on the set seed key, in the desensitized data interval to be sampled this time, randomly sampling intermediate desensitized data corresponding to the intermediate original data in the original data interval to be searched this time; wherein, the desensitized data interval to be sampled in the first binary search process is an ordered desensitized data set; and when the original data to be desensitized is different from the intermediate original data, half of the data interval in the desensitized data interval to be sampled this time, including the intermediate desensitized data, is used as the desensitized data interval to be sampled next time to continue the next random sampling, until the target original data corresponding to the original data to be desensitized is found, and the intermediate desensitized data is used as the target desensitized data corresponding to the target original data.

[0051] Further optionally, a method of using half of the data interval including the intermediate desensitized data in the desensitized data interval to be sampled this time as the desensitized data interval to be sampled next time to continue the next random sampling, until the target original data corresponding to the original data to be desensitized is found, and using the intermediate desensitized data as the target desensitized data corresponding to the target original data, including: if the original data to be desensitized is larger than the intermediate original data m_mid in the original data interval [m_min, m_max] to be found this time, then using the closed interval [c_mid, c_max] as the desensitized data interval [c_min, c_max] to be sampled next time to continue the next random sampling Random sampling is performed until the target original data corresponding to the original data to be desensitized is found, and the intermediate desensitized data c_mid is used as the target desensitized data corresponding to the target original data; if the original data to be desensitized is smaller than the intermediate original data m_mid in the original data interval [m_min, m_max] to be found this time, the closed interval [c_min, c_mid] is used as the next desensitized data interval [c_min, c_max] to be used to continue the next random sampling, until the target original data corresponding to the original data to be desensitized is found, and the intermediate desensitized data c_mid is used as the target desensitized data corresponding to the target original data. Among them, "c_min" represents the lower limit of the desensitized data interval, "c_max" represents the upper limit of the desensitized data interval, and "c_mid" represents the intermediate desensitized data.

[0052] In an optional embodiment, the process of randomly sampling the target desensitized data further includes at least one of the following operations:

[0053] Operation C1: If the upper limit and lower limit of the desensitized data interval to be sampled are the same, the upper limit or lower limit is directly used as the target desensitized data.

[0054] Operation C2: If the upper limit value and the lower limit value of the original data interval to be searched are the same, the desensitized data interval to be sampled contains only two desensitized data, and the intermediate desensitized data calculated based on the desensitized data interval to be sampled is equal to the upper limit value of the desensitized data interval to be sampled, then the intermediate desensitized data calculated based on the desensitized data interval to be sampled is directly used as the target desensitized data.

[0055] For operation C1, if the upper and lower limits of the desensitized data interval to be sampled are the same, that is, c_min=c_max, it means that all the desensitized data can only correspond to one desensitized data c_min or c_max after desensitization. Therefore, the upper or lower limit of the original data interval to be searched can be directly used as the target desensitized data. For operation C2, if the upper and lower limits of the original data interval to be searched are the same, the desensitized data interval to be sampled contains only two desensitized data, and the intermediate desensitized data calculated based on the desensitized data interval to be sampled is equal to the upper limit of the desensitized data interval to be sampled, that is, m_min=m_max, c_min+1=c_max and c_mid=c_max, so that the interval [m_min, m_max] will always contain m_mid, because m_mid=m_min=m_max, and the interval [c_min, c_max] will always contain c_mid, because each time c_mid is calculated, it is a decimal, and after rounding up, c_mid=c_max will hold. Therefore, when m_min=m_max, c_min+1=c_max and c_mid=c_max hold at the same time, it will continue to iterate in a loop. In order to jump out of the loop, the intermediate desensitized data calculated based on the desensitized data interval to be sampled can be directly used as the target desensitized data.

[0056] In this embodiment, no matter it is the original data interval or the desensitized data interval, the data interval used for each random sampling is a part of the data interval used for the previous random sampling. This ensures the boundedness of random sampling, so that the result after random sampling has a strict sampling range, so that the obtained desensitized data has a strict sampling range.

[0057] In an optional embodiment, a method for randomly sampling intermediate desensitized data corresponding to the intermediate original data in the original data interval to be searched this time in the desensitized data interval to be sampled based on a set seed key includes: using a bounded pseudo-random algorithm, randomly sampling the intermediate desensitized data corresponding to the intermediate original data in the original data interval to be searched this time in the desensitized data interval to be sampled this time. Among them, the bounded pseudo-random algorithm uses the set seed key as the random seed it uses, the desensitized data interval to be sampled this time as its output range, and p times the mean of the desensitized data interval to be sampled this time as its standard deviation, p is a configurable security parameter, and p>0, both integers and decimals are acceptable. The formula of the bounded pseudo-random algorithm is expressed as: r=Sample(k, C, σ). Where r represents the intermediate desensitized data or the target desensitized data; Sample represents the bounded pseudo-random algorithm, k, C, σ are the parameters of the bounded pseudo-random algorithm, k∈K, k is the seed key, K is the key space, C is the ordered desensitized data set, C=[c_min, c_max], σ is the standard deviation of the bounded pseudo-random algorithm, specifically: or In addition, |C|=c_max-c_min+1 represents the number of desensitized data in the ordered desensitized data set, then further, or

[0058] In this embodiment, in order to meet the availability, p times the mean of the desensitized data interval to be sampled this time is used as the standard deviation of the bounded pseudo-random algorithm, and p is a configurable security parameter. The larger the security parameter, the larger the standard deviation of the bounded pseudo-random algorithm, the greater the degree of deviation of the output desensitized data from the mean, the higher the security of the bounded pseudo-random algorithm, and the lower the availability; the smaller the security parameter, the smaller the standard deviation of the bounded pseudo-random algorithm, the smaller the degree of deviation of the output desensitized data from the mean, the lower the security of the bounded pseudo-random algorithm, and the higher the availability.

[0059] In some embodiments of the present application, taking the example that the original data to be desensitized, the original data in the ordered original data set, and the desensitized data in the ordered desensitized data set are all numerical data, the desensitization process of the embodiments of the present application is described in detail.

[0060] Among them, in order to simplify the description, the order-preserving desensitization process performed by the data processing end 102 is expressed as: c = OPD (k, M, C, m); its prerequisites: select a bounded pseudo-random sampling algorithm r = Sample (s, R, σ); set the key space K; set the availability parameter p∈ (0, +∞).

[0061] When receiving a call request initiated by the data source end 101, the data processing end 102 obtains the input parameters required for the above-mentioned order-preserving desensitization process c = OPD(k, M, C, m), that is, the set seed key k ∈ K, the ordered original data set M = [m_min, m_max], the ordered desensitized data set C = [c_min, c_max], and the original data m ∈ M to be desensitized. Among them, c ∈ C is the target desensitized data to be output.

[0062] After obtaining the input parameters, the data processing end 102 performs the desensitization process as follows:

[0063] (1) Calculate |M| = m_max - m_min + 1, |C| = c_max - c_min + 1. Where |M| represents the number of original data in the ordered desensitized data set, and |C| represents the number of desensitized data in the ordered desensitized data set.

[0064] (2) If |C| = 1, it means c_max = c_min, indicating that all data to be desensitized may only correspond to one desensitized data c_max or c_min. Then, take c_max or c_min as the target desensitized data to be output, and end this desensitization process.

[0065] (3) Calculate c_mid = Sample(k, C, σ), Or

[0066] (4) If |M| = 1, |C| = 2, and c_mid = c_max, for the interval [c_min, c_max], c_mid will always be included because after each calculation of c_mid, it is a decimal, and after the ceiling operation, c_mid = c_max will hold. Therefore, when m_min = m_max, c_min + 1 = c_max, and c_mid = c_max hold simultaneously, it will loop iteratively. To break out of the loop, the intermediate desensitized data calculated based on the current desensitized data interval to be sampled can be directly used as the target desensitized data, that is, take the current c_mid as the target desensitized data to be output, and end this desensitization process.

[0067] (5) Calculate Or

[0068] (6) If m_min ≤ m < m_mid, update M to M = [m_min, m_mid], update C to C = [c_min, c_mid], and return to step (1) to enter the next iteration process.

[0069] (7) If \(m_{mid}<m\leq m_{max}\), then update \(M\) to \(M = [m_{mid}, m_{max}]\), update \(C\) to \(C = [c_{mid}, c_{max}]\), and return to step (1) to enter the next iteration process.

[0070] (8) If \(m = m_{mid}\), then take \(c = c_{mid}\) as the target desensitized data to be output.

[0071] In the following scenario example, assume the data source end 101 is an e-commerce platform, which can generate data such as commodity sales amount, commodity sales volume, or commodity review count in various dimensions. These data belong to public domain data. Considering various reasons such as development, attracting users, or information disclosure, it is necessary to publish the public domain data generated by it. To avoid the leakage of sensitive information in the public domain data, the e-commerce platform can call the data processing end 102 to perform order-preserving desensitization on the raw data to be desensitized and output the order-preserving desensitized data. The following takes the ordered desensitized data set \(M = [1, 16]\), the ordered desensitized data set \(C = [1, 7]\), the data to be desensitized \(m = 3\), and the bounded pseudo-random algorithm is the Gaussian sampling algorithm as an example to illustrate the entire order-preserving desensitization process.

[0072] First iteration:

[0073] (1) Calculate \(|M| = m_{max}-m_{min}+1 = 16 - 1 + 1 = 16\), \(|C| = c_{max}-c_{min}+1 = 7 - 1 + 1 = 7\).

[0074] (2) Calculate \(c_{mid}=Sample(k, C, \sigma)=Sample(k, C, 4p)=4\).

[0075] (3) Calculate

[0076] (4) Judge \(1 = m_{min}<m = 3<m_{mid}=9\), then update \(M\) to \([1, 9]\), update \(C\) to \([1, 4]\), and enter the second iteration.

[0077] Second iteration:

[0078] (1) Calculate \(|M| = m_{max}-m_{min}+1 = 9 - 1 + 1 = 9\), \(|C| = c_{max}-c_{min}+1 = 4 - 1 + 1 = 4\).

[0079] (2) Calculate \(c_{mid}=Sample(k, C, \sigma)=Sample(k, C, 2p)=2\).

[0080] (3) Calculate

[0081] (4) If it is determined that 1 = m_min < m = 3 < m_mid = 5, then update M to [1, 5], update C to [1, 2], and enter the third iteration.

[0082] Third iteration:

[0083] (1) Calculate |M| = m_max - m_min + 1 = 5 - 1 + 1 = 5, |C| = c_max - c_min + 1 = 2 - 1 + 1 = 2.

[0084] (2) Calculate c_mid = Sample(k, C, σ) = Sample(k, C, p) = 2.

[0085] (3) Calculate

[0086] (4) If m = m_mid = 3, then use c_mid = 2 as the target desensitized data to be output.

[0087] It can be seen that through the above three iterative processes, the data to be desensitized 3 can be mapped to the desensitized data 2. For the user, 2 can be seen instead of directly seeing 3. And because the order between the desensitized data 2 corresponding to the data to be desensitized 3 and the desensitized data corresponding to other data to be desensitized is retained, the user can fully perform various operations such as size comparison, sorting, and numerical analysis on the desensitized data, ensuring data availability while ensuring data security.

[0088] In the above system embodiment of the present application, the data processing end 102 can be implemented as an application program, plug-in, patch, service, instance, functional module or library function in software form with a data desensitization function, and can be called by an application such as the data source end 101 that has a desensitization requirement. However, the data processing end 102 is not limited to this implementation form. For example, in some other embodiments of the present application, the order-preserving desensitization function implemented by the data processing end 102 can be embedded in the data source end 101 as a certain functional module of the data source end 101 to implement the order-preserving desensitization function of the data source end 101. Then, from the perspective of the data source end with the order-preserving desensitization function, the process of implementing order-preserving desensitization includes: the data source end obtains the original data to be desensitized generated during its operation; in the ordered original data set corresponding to the original data to be desensitized, searches for the target original data corresponding to the original data to be desensitized; and based on the set seed key, randomly samples the target desensitized data corresponding to the target original data in the ordered desensitized data set corresponding to the ordered original data set; outputs the target desensitized data; where the target desensitized data randomly sampled based on the seed key retains the order between the original data to be desensitized.

[0089] In this embodiment, the data source end with the data order-preserving desensitization function obtains the original data to be desensitized generated during the operation process, which is not limited to the implementation method. In an optional embodiment, the data source end with the data order-preserving desensitization function can directly obtain the original data to be desensitized from its designated storage space, and the designated storage space is specifically used to store the original data to be desensitized. In another optional embodiment, the data source end with the data order-preserving desensitization function can display a desensitization configuration interface; in response to the user's first input operation on the desensitization configuration interface, obtain the data parameters input by the user pointing to the original data to be desensitized; according to the data parameters, obtain the original data to be desensitized from the generated data set.

[0090] In this embodiment, the first input operation is an input operation, and the input operation may be, but is not limited to, keyboard input or electronic screen input, etc. The content input by the first input operation includes data parameters of the original data to be desensitized, and the data parameters of the original data to be desensitized may be, but are not limited to, storage location information of the original data to be desensitized or attribute information of the original data to be desensitized, etc. For details, please refer to the aforementioned embodiments, which will not be repeated here. In addition, the user refers to the data management personnel or maintenance personnel at the data source end.

[0091] Further optionally, the data source end with the data order-preserving desensitization function can also respond to the second input operation of the user on the desensitization configuration interface, and obtain the ordered original data set corresponding to the original data to be desensitized, the ordered desensitized data set corresponding to the ordered original data set, and the set seed key input by the user. Optionally, the user can estimate the ordered original data set based on the maximum and minimum values ​​corresponding to the original data to be desensitized; accordingly, based on the maximum and minimum values ​​corresponding to the original data to be desensitized, the upper and lower limits of the ordered desensitized data set are estimated, and then based on the upper and lower limits of the ordered desensitized data set and the set data step, the ordered desensitized data set is obtained.

[0092] In this embodiment, the second input operation is an input operation, which may be, but is not limited to, keyboard input or electronic screen input, etc. The content input by the second input operation includes the ordered original data set corresponding to the original data to be desensitized, the ordered desensitized data set corresponding to the ordered original data set, and the information of the set seed key.

[0093] For the detailed process of the data source end with data order-preserving desensitization function searching for the target original data corresponding to the original data to be desensitized and randomly sampling the target desensitized data corresponding to the target original data, please refer to the aforementioned embodiment. The only difference is that the execution subject is different, so it will not be repeated here.

[0094] Figure 2a A flowchart of a data processing method provided for an exemplary embodiment of the present application is shown in FIG. Figure 2aAs shown, the method includes:

[0095] 21. According to the call request initiated by the data source, obtain the original data to be desensitized from the data source;

[0096] 22. In the ordered original data set corresponding to the original data to be desensitized, search for the target original data corresponding to the original data to be desensitized;

[0097] 23. Based on the set seed key, randomly sample the target desensitized data corresponding to the target original data in the ordered desensitized data set corresponding to the ordered original data set;

[0098] 24. Return the target desensitized data to the data source end so that the data source end can output the target desensitized data; wherein, the target desensitized data randomly sampled based on the seed key retains the orderliness between the original data to be desensitized.

[0099] In an optional embodiment, searching for target original data corresponding to the original data to be desensitized in an ordered original data set corresponding to the original data to be desensitized includes: using a binary search method to search for target original data corresponding to the original data to be desensitized in the ordered original data set. Accordingly, based on a set seed key, randomly sampling target desensitized data corresponding to the target original data in an ordered desensitized data set corresponding to the ordered original data set includes: using a binary search method to randomly sample target desensitized data corresponding to the target original data in an ordered desensitized data set based on a set seed key.

[0100] In an optional embodiment, a binary search method is used to search for target original data corresponding to the original data to be desensitized in an ordered original data set, including: in each binary search process, the original data to be desensitized is compared with the intermediate original data in the original data interval to be searched this time; wherein, the original data interval to be searched in the first binary search process is an ordered original data set; if the original data to be desensitized is different from the intermediate original data, then half of the data interval including the intermediate original data in the original data interval to be searched this time is used as the original data interval to be searched next time to continue the next binary search, until the intermediate original data identical to the original data to be desensitized is found as the target original data.

[0101] Further optionally, half of the data intervals including the intermediate original data in the original data interval to be searched this time are used as the original data interval to be searched next time to continue the next binary search, until the intermediate original data identical to the original data to be desensitized is found as the target original data, including: if the original data to be desensitized is larger than the intermediate original data m_mid in the original data interval [m_min, m_max] to be searched this time, then the closed interval [m_mid, m_max] is used as the original data interval [m_min, m_max] to be searched next time to continue the next binary search, until the intermediate original data identical to the original data to be desensitized is found as the target original data; if the original data to be desensitized is smaller than the intermediate original data m_mid in the original data interval [m_min, m_max] to be searched this time, then the closed interval [m_min, m_mid] is used as the original data interval [m_min, m_max] to be searched next time to continue the next binary search, until the intermediate original data identical to the original data to be desensitized is found as the target original data.

[0102] In an optional embodiment, a binary search method is adopted, and based on a set seed key, target desensitized data corresponding to the target original data is randomly sampled in an ordered desensitized data set, including: in each binary search process, based on the set seed key, in the desensitized data interval to be sampled this time, intermediate desensitized data corresponding to the intermediate original data in the original data interval to be searched this time is randomly sampled; wherein, the desensitized data interval to be sampled in the first binary search process is an ordered desensitized data set; and when the original data to be desensitized is different from the intermediate original data, half of the data interval in the desensitized data interval to be sampled this time, including the intermediate desensitized data, is used as the desensitized data interval to be sampled next time to continue the next random sampling, until the target original data corresponding to the original data to be desensitized is found, and the intermediate desensitized data is used as the target desensitized data corresponding to the target original data.

[0103] Further optionally, half of the data interval including the intermediate desensitized data in the desensitized data interval to be sampled this time is used as the desensitized data interval to be sampled next time to continue the next random sampling, until the target original data corresponding to the original data to be desensitized is found, and the intermediate desensitized data is used as the target desensitized data corresponding to the target original data, including: if the original data to be desensitized is larger than the intermediate original data m_mid in the original data interval [m_min, m_max] to be found this time, then the closed interval [c_mid, c_max] is used as the desensitized data interval [c_min, c_max] to be sampled next time to continue the next random sampling Random sampling is performed until the target original data corresponding to the original data to be desensitized is found, and the intermediate desensitized data c_mid is used as the target desensitized data corresponding to the target original data; if the original data to be desensitized is smaller than the intermediate original data m_mid in the original data interval [m_min, m_max] to be found this time, the closed interval [c_min, c_mid] is used as the next desensitized data interval [c_min, c_max] to be adopted to continue the next random sampling, until the target original data corresponding to the original data to be desensitized is found, and the intermediate desensitized data c_mid is used as the target desensitized data corresponding to the target original data.

[0104] In an optional embodiment, based on a set seed key, in the desensitized data interval to be sampled this time, intermediate desensitized data corresponding to the intermediate original data in the original data interval to be searched this time is randomly sampled, including: using a bounded pseudo-random algorithm, in the desensitized data interval to be sampled this time, randomly sampling the intermediate desensitized data corresponding to the intermediate original data in the original data interval to be searched this time; wherein the bounded pseudo-random algorithm uses the set seed key as the random seed it uses, the desensitized data interval to be sampled this time as its output range, and p times the mean of the desensitized data interval to be sampled this time as its standard deviation, p is a configurable security parameter, and p>0.

[0105] In an optional embodiment, the process of randomly sampling the target desensitized data also includes at least one of the following operations: if the upper limit value and the lower limit value of the desensitized data interval to be sampled this time are the same, the upper limit value or the lower limit value is directly used as the target desensitized data; if the upper limit value and the lower limit value of the original data interval to be searched this time are the same, the desensitized data interval to be sampled this time contains only two desensitized data, and the intermediate desensitized data calculated based on the desensitized data interval to be sampled this time is equal to the upper limit value of the desensitized data interval to be sampled this time, then the intermediate desensitized data calculated based on the desensitized data interval to be sampled this time is directly used as the target desensitized data.

[0106] In an optional embodiment, before searching for the target original data corresponding to the original data to be desensitized in the ordered original data set corresponding to the original data to be desensitized, it also includes: obtaining the ordered original data set corresponding to the original data to be desensitized, the ordered desensitized data set corresponding to the ordered original data set, and the set seed key provided by the data source; or reading the preset ordered original data set corresponding to the original data to be desensitized, the ordered desensitized data set corresponding to the ordered original data set, and the set seed key from the designated data storage space.

[0107] In an optional embodiment, the number of data included in the above-mentioned original data set is greater than the number of data included in the ordered desensitized data set.

[0108] In an optional embodiment, the original data to be desensitized, the original data in the ordered original data set, and the desensitized data in the ordered desensitized data set are all numerical data.

[0109] The embodiment of the present application provides an order-preserving desensitization method, in which an ordered original data set corresponding to the original data to be desensitized and an ordered desensitized data set corresponding to the ordered original data set are provided; in the desensitization process, the target original data corresponding to the original data to be desensitized is searched in the ordered original data set, and then based on the set seed key, the target desensitized data corresponding to the target original data is randomly sampled in the ordered desensitized data set corresponding to the ordered original data set as the desensitization result of the original data to be desensitized. In this scheme, the target desensitized data randomly sampled based on the seed key can retain the orderliness between the original data to be desensitized, thereby realizing order-preserving desensitization, which enables the data user to perform subsequent processing based on the orderliness retained by the desensitized data, and on the basis of improving data security, the availability of the data can also be guaranteed.

[0110] This application Figure 2a The method shown can be applied to e-commerce scenarios. In e-commerce scenarios, it is necessary for e-commerce platforms to publish their public domain data to merchants. Public domain data is relatively sensitive data within the e-commerce platform, and direct disclosure to merchants may pose data security risks. Therefore, it is necessary to perform order-preserving desensitization on public domain data, thereby providing merchants with desensitized data with a certain degree of usability. This can not only provide merchants with relevant data value, but also ensure that it is difficult for merchants or other users or platforms to infer the real public domain data based on the desensitized data, thereby controlling data security risks. Based on this, an embodiment of the present application also provides a data publishing method based on ordered desensitization, such as Figure 2b As shown, the method includes:

[0111] 201. According to the call request initiated by the e-commerce platform, obtain the original public domain data that the e-commerce platform needs to release to the merchants.

[0112] 202. Search for target public domain data corresponding to the original public domain data in the ordered public domain data set corresponding to the original public domain data.

[0113] 203. Based on the set seed key, randomly sample target desensitized data corresponding to the target public domain data from the ordered desensitized data set corresponding to the ordered public domain data set.

[0114] 204. Return the target desensitized data to the e-commerce platform, so that the e-commerce platform can use the target desensitized data to replace the original public domain data and then release it to the merchants; wherein, the target desensitized data randomly sampled based on the seed key retains the orderliness between the original public domain data.

[0115] In this embodiment, the data within the e-commerce platform is referred to as public domain data, including, but not limited to, at least one of: industry sales amount, industry sales volume, category sales amount, category sales volume, and platform visits. These public domain data have a certain reference value for merchants, and can help merchants understand the current status of the industry and the popularity of various categories in the industry. Therefore, it is necessary to release these public domain data to merchants to guide them.

[0116] In this embodiment, before the e-commerce platform releases the public domain data to the merchant, it can initiate a call request to the data processing end, and request to call the data desensitization service to perform orderly desensitization processing on the original public domain data. The data processing end obtains the public domain data to be released according to the call request of the e-commerce platform; and then performs the orderly desensitization operation described in steps 202 and 203 to orderly desensitize the original public domain data, and returns the target desensitized data obtained after orderly desensitization to the e-commerce platform. The e-commerce platform releases the target desensitized data to the merchant instead of the original public domain data, which can ensure that the merchant or other users or platforms are difficult to reverse the real public domain data based on the desensitized data, and control the data security risk. In addition, based on the characteristics of orderly desensitization, the target desensitized data retains the orderliness between the original public domain data. Therefore, for the merchant, the target desensitized data is still of guiding significance to it, and the information such as the current status of the industry and the popularity of various categories in the industry can still be understood according to the target desensitized data, so as to achieve the purpose of providing relevant data value for the merchant.

[0117] in, Figure 2b The ordered public domain data set in is a specific implementation of the ordered original data set in the above embodiment. Figure 2b The detailed implementation of each step in the illustrated method embodiment can be found in the aforementioned embodiment and will not be described in detail here.

[0118] Figure 3a A flowchart of another data processing method provided for an exemplary embodiment of the present application is shown in FIG. Figure 3a As shown, the method includes:

[0119] 31. Obtain the original data to be desensitized generated during the operation;

[0120] 32. In the ordered original data set corresponding to the original data to be desensitized, search for the target original data corresponding to the original data to be desensitized;

[0121] 33. Based on the set seed key, randomly sample the target desensitized data corresponding to the target original data in the ordered desensitized data set corresponding to the ordered original data set;

[0122] 34. Output the target desensitized data; wherein, the target desensitized data randomly sampled based on the seed key retains the orderliness between the original data to be desensitized.

[0123] In an optional embodiment, the original data to be desensitized generated during the operation process is obtained, including: displaying a desensitization configuration interface corresponding to the target application; responding to a first input operation of the user on the desensitization configuration interface, obtaining data parameters input by the user pointing to the original data to be desensitized; and obtaining the original data to be desensitized from the data set generated by the target application according to the data parameters.

[0124] In an optional embodiment, the method provided in this embodiment also includes: responding to a second input operation of the user on the desensitizing configuration interface, obtaining an ordered original data set corresponding to the original data to be desensitized input by the user, an ordered desensitized data set corresponding to the ordered original data set, and a set seed key.

[0125] The embodiment of the present application provides an order-preserving desensitization method, in which an ordered original data set corresponding to the original data to be desensitized and an ordered desensitized data set corresponding to the ordered original data set are provided; in the desensitization process, the target original data corresponding to the original data to be desensitized is searched in the ordered original data set, and then based on the set seed key, the target desensitized data corresponding to the target original data is randomly sampled in the ordered desensitized data set corresponding to the ordered original data set as the desensitization result of the original data to be desensitized. In this scheme, the target desensitized data randomly sampled based on the seed key can retain the orderliness between the original data to be desensitized, thereby realizing order-preserving desensitization, which enables the data user to perform subsequent processing based on the orderliness retained by the desensitized data, and on the basis of improving data security, the availability of the data can also be guaranteed.

[0126] This application Figure 3a The method shown can be applied in e-commerce scenarios. Based on this, the embodiment of the present application also provides a data publishing method based on orderly desensitization, such as Figure 3b As shown, the method includes:

[0127] 301. The e-commerce platform obtains original public domain data that needs to be released to merchants.

[0128] 302. Search for target public domain data corresponding to the original public domain data in the ordered public domain data set corresponding to the original public domain data.

[0129] 303. Based on the set seed key, randomly sample target desensitized data corresponding to the target public domain data in the ordered desensitized data set corresponding to the ordered public domain data set.

[0130] 304. The target desensitized data is used to replace the original public domain data and then released to the merchant; wherein the target desensitized data randomly sampled based on the seed key retains the orderliness between the original public domain data.

[0131] In this embodiment, the data within the e-commerce platform is referred to as public domain data, including, but not limited to, at least one of: industry sales amount, industry sales volume, category sales amount, category sales volume, and platform visits. These public domain data have a certain reference value for merchants, and can help merchants understand the current status of the industry and the popularity of various categories in the industry. Therefore, it is necessary to release these public domain data to merchants to guide them.

[0132] In this embodiment, in addition to the data publishing function, the e-commerce platform also has an orderly desensitization function. Based on this, before the e-commerce platform releases the public domain data to the merchant, it can perform the orderly desensitization operation described in steps 302 and 303 to orderly desensitize the original public domain data; afterwards, the e-commerce platform releases the target desensitized data to the merchant instead of the original public domain data, which can ensure that it is difficult for the merchant or other users or platforms to reverse the real public domain data based on the desensitized data, and control the data security risk. In addition, based on the characteristics of orderly desensitization, the target desensitized data retains the orderliness between the original public domain data. Therefore, for the merchant, the target desensitized data is still of guiding significance to it, and it can still understand the current situation of the industry and the popularity of various categories in the industry based on the target desensitized data, and achieve the purpose of providing relevant data value to the merchant.

[0133] in, Figure 3b The ordered public domain data set in is a specific implementation of the ordered original data set in the above embodiment. Figure 3b The detailed implementation of each step in the illustrated method embodiment can be found in the aforementioned embodiment and will not be described in detail here.

[0134] It should be noted that the execution subject of each step of the method provided in the above embodiment can be the same device, or the method can be executed by different devices. For example, the execution subject of steps 21 to 23 can be device A; for another example, the execution subject of steps 21 and 22 can be device A, and the execution subject of step 23 can be device B; and so on.

[0135] In addition, in some of the processes described in the above embodiments and the accompanying drawings, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel, and the sequence numbers of the operations, such as 21, 22, etc., are only used to distinguish between different operations, and the sequence numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0136] Figure 4 The following is a schematic diagram of a data processing device provided by an exemplary embodiment of the present application. Figure 4 As shown, the device includes: a memory 44 , a processor 45 and a communication component 46 .

[0137] The memory 44 is used to store computer programs and can be configured to store various other data to support operations on the data processing device. Examples of such data include instructions for any application or method operating on the data processing device, contact data, phone book data, messages, pictures, videos, etc.

[0138] The memory 44 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0139] The processor 45 is coupled to the memory 44 and is used to execute the computer program in the memory 44, so as to: obtain the original data to be desensitized from the data source through the communication component 46 according to the call request initiated by the data source; search for the target original data corresponding to the original data to be desensitized in the ordered original data set corresponding to the original data to be desensitized; and based on the set seed key, randomly sample the target desensitized data corresponding to the target original data in the ordered desensitized data set corresponding to the ordered original data set; return the target desensitized data to the data source so that the data source outputs the target desensitized data; wherein the target desensitized data randomly sampled based on the seed key retains the orderliness between the original data to be desensitized.

[0140] In an optional embodiment, when the processor 45 searches for the target original data corresponding to the original data to be desensitized in the ordered original data set corresponding to the original data to be desensitized, it is specifically used to: use a binary search method to search for the target original data corresponding to the original data to be desensitized in the ordered original data set. Correspondingly, when the processor 45 randomly samples the target desensitized data corresponding to the target original data in the ordered desensitized data set corresponding to the ordered original data set based on the set seed key, it is specifically used to: use a binary search method to randomly sample the target desensitized data corresponding to the target original data in the ordered desensitized data set based on the set seed key.

[0141] In an optional embodiment, when the processor 45 uses binary search to search for target original data corresponding to the original data to be desensitized in an ordered original data set, it is specifically used to: in each binary search process, compare the original data to be desensitized with the intermediate original data in the original data interval to be searched this time; wherein, the original data interval to be searched in the first binary search process is an ordered original data set; if the original data to be desensitized is different from the intermediate original data, then half of the data interval including the intermediate original data in the original data interval to be searched this time is used as the original data interval to be searched next time to continue the next binary search, until the intermediate original data identical to the original data to be desensitized is found as the target original data.

[0142] In an optional embodiment, the processor 45 uses half of the data interval including the intermediate original data in the original data interval to be searched this time as the original data interval to be searched next time to continue the next binary search until the intermediate original data identical to the original data to be desensitized is found as the target original data. Specifically, if the original data to be desensitized is larger than the intermediate original data m_mid in the original data interval [m_min, m_max] to be searched this time, the closed interval [m_mid, m_max] is used as the original data interval [m_min, m_max] to be searched next time to continue the next binary search until the intermediate original data identical to the original data to be desensitized is found as the target original data; if the original data to be desensitized is smaller than the intermediate original data m_mid in the original data interval [m_min, m_max] to be searched this time, the closed interval [m_min, m_mid] is used as the original data interval [m_min, m_max] to be searched next time to continue the next binary search until the intermediate original data identical to the original data to be desensitized is found as the target original data.

[0143] In an optional embodiment, when the processor 45 adopts binary search and randomly samples the target desensitized data corresponding to the target original data in the ordered desensitized data set based on the set seed key, it is specifically used to: in each binary search process, based on the set seed key, randomly sample the intermediate desensitized data corresponding to the intermediate original data in the original data interval to be searched in the desensitized data interval to be sampled this time; wherein, the desensitized data interval to be sampled in the first binary search process is an ordered desensitized data set; and when the original data to be desensitized is different from the intermediate original data, half of the data interval in the desensitized data interval to be sampled this time, including the intermediate desensitized data, is used as the desensitized data interval to be sampled next time to continue the next random sampling, until the target original data corresponding to the original data to be desensitized is found, and the intermediate desensitized data is used as the target desensitized data corresponding to the target original data.

[0144] In an optional embodiment, the processor 45 uses half of the data interval including the intermediate desensitized data in the desensitized data interval to be sampled this time as the desensitized data interval to be sampled next time to continue the next random sampling until the target original data corresponding to the original data to be desensitized is found, and uses the intermediate desensitized data as the target desensitized data corresponding to the target original data. Specifically, if the original data to be desensitized is larger than the intermediate original data m_mid in the original data interval [m_min, m_max] to be found this time, then the closed interval [c_mid, c_max] is used as the desensitized data interval [c_min, c_max] to be sampled next time to continue Perform the next random sampling until the target original data corresponding to the original data to be desensitized is found, and use the intermediate desensitized data c_mid as the target desensitized data corresponding to the target original data; if the original data to be desensitized is smaller than the intermediate original data m_mid in the original data interval [m_min, m_max] to be found this time, use the closed interval [c_min, c_mid] as the next desensitized data interval [c_min, c_max] to be adopted, and continue the next random sampling until the target original data corresponding to the original data to be desensitized is found, and use the intermediate desensitized data c_mid as the target desensitized data corresponding to the target original data.

[0145] In an optional embodiment, when the processor 45 randomly samples the intermediate desensitized data corresponding to the intermediate original data in the original data interval to be searched this time in the desensitized data interval to be sampled based on the set seed key, it is specifically used to: adopt a bounded pseudo-random algorithm to randomly sample the intermediate desensitized data corresponding to the intermediate original data in the original data interval to be searched this time in the desensitized data interval to be sampled this time; wherein the bounded pseudo-random algorithm uses the set seed key as the random seed it uses, the desensitized data interval to be sampled this time as its output range, and p times the mean of the desensitized data interval to be sampled this time as its standard deviation, p is a configurable security parameter, and p>0.

[0146] In an optional embodiment, the processor 45 is specifically used to: in the process of randomly sampling the target desensitized data, it also includes at least one of the following operations: if the upper limit value and the lower limit value of the desensitized data interval to be sampled this time are the same, the upper limit value or the lower limit value is directly used as the target desensitized data; if the upper limit value and the lower limit value of the original data interval to be searched this time are the same, the desensitized data interval to be sampled this time only contains two desensitized data, and the intermediate desensitized data calculated based on the desensitized data interval to be sampled this time is equal to the upper limit value of the desensitized data interval to be sampled this time, then the intermediate desensitized data calculated based on the desensitized data interval to be sampled this time is directly used as the target desensitized data.

[0147] In an optional embodiment, before searching for the target original data corresponding to the original data to be desensitized in the ordered original data set corresponding to the original data to be desensitized, the processor 45 is also used to: obtain the ordered original data set corresponding to the original data to be desensitized, the ordered desensitized data set corresponding to the ordered original data set, and the set seed key provided by the data source; or read the preset ordered original data set corresponding to the original data to be desensitized, the ordered desensitized data set corresponding to the ordered original data set, and the set seed key from the designated data storage space.

[0148] It should be noted that the data processing device of this embodiment can perform desensitization processing on the public domain data to be released in the e-commerce scenario. The public domain data includes at least one of the industry sales amount, industry sales volume, category sales amount, category sales volume and platform visits within the e-commerce platform. Based on this, the processor 45 is also used to:

[0149] Receive the call request from the e-commerce platform; obtain the original public domain data that the e-commerce platform needs to release to the merchant according to the call request initiated by the e-commerce platform; search for the target public domain data corresponding to the original public domain data in the ordered public domain data set corresponding to the original public domain data; based on the set seed key, randomly sample the target desensitized data corresponding to the target public domain data in the ordered desensitized data set corresponding to the ordered public domain data set; return the target desensitized data to the e-commerce platform, so that the e-commerce platform can replace the original public domain data with the target desensitized data and then release it to the merchant; wherein, the target desensitized data randomly sampled based on the seed key retains the orderliness between the original public domain data.

[0150] Further, if Figure 4 As shown, the data processing device also includes: a display 47, a power supply component 48, an audio component 49 and other components. Figure 4 Only some components are shown schematically, which does not mean that the data processing device only includes Figure 4 In addition, Figure 4 The components shown in the dashed box are optional components, not mandatory components, and may depend on the implementation form of the data processing device. If the data processing device is implemented as a traditional server, cloud server, server array, etc., the components shown in the dashed box may not be included.

[0151] Accordingly, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, which, when executed, can implement the steps that can be executed by the data processing device in the above data processing method embodiment.

[0152] Figure 5 A schematic diagram of the structure of another data processing device provided by an exemplary embodiment of the present application. Figure 5 As shown, the device includes: a memory 54 and a processor 55.

[0153] The memory 54 is used to store computer programs and can be configured to store various other data to support operations on the data processing device. Examples of such data include instructions for any application or method operating on the data processing device, contact data, phone book data, messages, pictures, videos, etc.

[0154] The memory 54 can be implemented by any type of volatile or nonvolatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0155] The processor 55 is coupled to the memory 54 and is used to execute the computer program in the memory 54, so as to: obtain the original data to be desensitized generated by the data processing device during operation; search for the target original data corresponding to the original data to be desensitized in the ordered original data set corresponding to the original data to be desensitized; and based on the set seed key, randomly sample the target desensitized data corresponding to the target original data in the ordered desensitized data set corresponding to the ordered original data set; output the target desensitized data; wherein the target desensitized data randomly sampled based on the seed key retains the orderliness between the original data to be desensitized.

[0156] In an optional embodiment, when the processor 55 obtains the original data to be desensitized generated by the data processing device during operation, it is specifically used to: display the desensitization configuration interface corresponding to the target application; respond to the user's first input operation on the desensitization configuration interface, and obtain the data parameters input by the user pointing to the original data to be desensitized; and obtain the original data to be desensitized from the data set generated by the target application according to the data parameters.

[0157] In an optional embodiment, the processor 55 is also used to: respond to the user's second input operation on the desensitization configuration interface, obtain the ordered original data set corresponding to the original data to be desensitized input by the user, the ordered desensitized data set corresponding to the ordered original data set, and the set seed key.

[0158] It should be noted that the data processing device of this embodiment can be implemented as an e-commerce platform in an e-commerce scenario, and can perform desensitization processing on the public domain data to be published. The public domain data includes at least one of the industry sales amount, industry sales volume, category sales amount, category sales volume and platform visits within the e-commerce platform. Based on this, the processor 55 is also used to:

[0159] The original public domain data to be released to the merchant is obtained; in the ordered public domain data set corresponding to the original public domain data, the target public domain data corresponding to the original public domain data is searched; based on the set seed key, in the ordered desensitized data set corresponding to the ordered public domain data set, the target desensitized data corresponding to the target public domain data is randomly sampled; the original public domain data is replaced by the target desensitized data and then released to the merchant; wherein the target desensitized data randomly sampled based on the seed key retains the orderliness between the original public domain data.

[0160] Further, if Figure 5 As shown, the data processing device also includes: a communication component 56, a display 57, a power component 58, an audio component 59 and other components. Figure 5 Only some components are shown schematically, which does not mean that the data processing device only includes Figure 5 In addition, Figure 5The components shown in the dashed box are optional components, not mandatory components, and may depend on the implementation form of the data processing device. If the data processing device is implemented as a traditional server, cloud server, server array, etc., the components shown in the dashed box may not be included.

[0161] Accordingly, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, which, when executed, can implement the steps that can be executed by the data processing device in the above data processing method embodiment.

[0162] Above Figure 4 and Figure 5 The communication component in is configured to facilitate wired or wireless communication between the device where the communication component is located and other devices. The device where the communication component is located can access a wireless network based on a communication standard, such as WiFi, 2G, 3G, 4G / LTE, 5G and other mobile communication networks, or a combination thereof. In an exemplary embodiment, the communication component receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0163] Above Figure 4 and Figure 5 The display in the embodiment includes a screen, which may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation.

[0164] Above Figure 4 and Figure 5 The power supply component in the device provides power to various components of the device where the power supply component is located. The power supply component may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device where the power supply component is located.

[0165] Above Figure 4 and Figure 5The audio component in can be configured to output and / or input audio signals. For example, the audio component includes a microphone (MIC), and when the device where the audio component is located is in an operating mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in a memory or sent via a communication component. In some embodiments, the audio component also includes a speaker for outputting an audio signal.

[0166] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0167] The present application is described with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, and the combination of the process and / or box in the flowchart and / or block diagram can be realized by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for realizing the function specified in one process or multiple processes in the flowchart and / or one box or multiple boxes in the block diagram.

[0168] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0169] These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0170] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0171] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0172] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0173] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0174] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A data processing method, characterized in that: include: According to the call request initiated by the data source, the original data to be desensitized from the data source is obtained; Searching for target original data corresponding to the original data to be desensitized in the ordered original data set corresponding to the original data to be desensitized; as well as Based on the set seed key, randomly sampling target desensitized data corresponding to the target original data in the ordered desensitized data set corresponding to the ordered original data set; Return the target desensitized data to the data source end so that the data source end can output the target desensitized data; wherein, the original data in the ordered original data set are sorted by size, the desensitized data in the ordered desensitized data set are sorted by size, and the original data in the ordered original data set are mapped to the desensitized data in the ordered desensitized data set according to the size relationship, and the target desensitized data randomly sampled based on the seed key retains the orderliness between the original data to be desensitized.

2. The method according to claim 1, characterized in that Searching for target original data corresponding to the original data to be desensitized in the ordered original data set corresponding to the original data to be desensitized, comprising: using a binary search method to search for target original data corresponding to the original data to be desensitized in the ordered original data set; Correspondingly, based on the set seed key, in the ordered desensitized data set corresponding to the ordered original data set, the target desensitized data corresponding to the target original data is randomly sampled, including: using a binary search method, based on the set seed key, in the ordered desensitized data set, the target desensitized data corresponding to the target original data is randomly sampled.

3. The method according to claim 2, characterized in that Using a binary search method, in the ordered original data set, the target original data corresponding to the original data to be desensitized is searched, including: In each binary search process, the original data to be desensitized is compared with the intermediate original data in the original data interval to be searched this time; wherein the original data interval to be searched in the first binary search process is the ordered original data set; If the original data to be desensitized is different from the intermediate original data, half of the data interval including the intermediate original data in the original data interval to be searched this time will be used as the next original data interval to be searched to continue the next binary search until the intermediate original data that is the same as the original data to be desensitized is found as the target original data.

4. The method according to claim 3, characterized in that The half of the data intervals in the original data interval to be searched this time, including the intermediate original data, are used as the original data interval to be searched next time to continue the next binary search until the intermediate original data identical to the original data to be desensitized is found as the target original data, including: If the original data to be desensitized is larger than the intermediate original data m_mid in the original data interval [m_min, m_max] to be searched this time, the closed interval [m_mid, m_max] is used as the next original data interval [m_min, m_max] to be searched, and the next binary search is continued until the intermediate original data identical to the original data to be desensitized is found as the target original data; If the original data to be desensitized is smaller than the intermediate original data m_mid in the original data interval [m_min, m_max] to be searched this time, the closed interval [m_min, m_mid] will be used as the next original data interval [m_min, m_max] to be searched, and the next binary search will be continued until the intermediate original data identical to the original data to be desensitized is found as the target original data.

5. The method according to claim 3, characterized in that: Using a binary search method, based on a set seed key, in the ordered desensitized data set, target desensitized data corresponding to the target original data is randomly sampled, including: In each binary search process, based on the set seed key, in the desensitized data interval to be sampled this time, the intermediate desensitized data corresponding to the intermediate original data in the original data interval to be searched this time is randomly sampled; wherein the desensitized data interval to be searched and sampled in the first binary search process is the ordered desensitized data set; and When the original data to be desensitized is different from the intermediate original data, half of the data interval including the intermediate desensitized data in the desensitized data interval to be sampled this time will be used as the desensitized data interval to be sampled next time to continue the next random sampling, until the target original data corresponding to the original data to be desensitized is found, and the intermediate desensitized data will be used as the target desensitized data corresponding to the target original data.

6. The method according to claim 5, characterized in that The method further comprises: using half of the data interval including the intermediate desensitized data in the desensitized data interval to be sampled this time as the desensitized data interval to be sampled next time, and continuing the next random sampling until the target original data corresponding to the original data to be desensitized is found, and using the intermediate desensitized data as the target desensitized data corresponding to the target original data, including: If the original data to be desensitized is larger than the intermediate original data m_mid in the original data interval [m_min, m_max] to be searched this time, the closed interval [c_mid, c_max] is used as the next desensitized data interval [c_min, c_max] to be sampled, and the next random sampling is continued until the target original data corresponding to the original data to be desensitized is found, and the intermediate desensitized data c_mid is used as the target desensitized data corresponding to the target original data; If the original data to be desensitized is smaller than the intermediate original data m_mid in the original data interval [m_min, m_max] to be searched this time, the closed interval [c_min, c_mid] will be used as the next desensitized data interval [c_min, c_max] to be adopted and the next random sampling will be continued until the target original data corresponding to the original data to be desensitized is found, and the intermediate desensitized data c_mid will be used as the target desensitized data corresponding to the target original data.

7. The method according to claim 5, characterized in that Based on the set seed key, in the desensitized data interval to be sampled this time, the intermediate desensitized data corresponding to the intermediate original data in the original data interval to be searched this time is randomly sampled, including: Adopting a bounded pseudo-random algorithm, in the interval of desensitized data to be sampled this time, randomly sample the intermediate desensitized data corresponding to the intermediate original data in the interval of original data to be searched this time; Among them, the bounded pseudo-random algorithm uses the set seed key as the random seed it adopts, the desensitized data interval to be sampled this time as its output range, and p times the mean of the desensitized data interval to be sampled this time as its standard deviation, where p is a configurable security parameter, and p>0.

8. The method according to claim 7, characterized in that The process of randomly sampling the target desensitized data also includes at least one of the following operations: If the upper limit and lower limit of the desensitized data interval to be sampled this time are the same, the upper limit or lower limit is directly used as the target desensitized data; If the upper limit value and the lower limit value of the original data interval to be searched this time are the same, the desensitized data interval to be sampled this time contains only two desensitized data, and the intermediate desensitized data calculated based on the desensitized data interval to be sampled this time is equal to the upper limit value of the desensitized data interval to be sampled this time, then the intermediate desensitized data calculated based on the desensitized data interval to be sampled this time is directly used as the target desensitized data.

9. The method according to any one of claims 1 to 8, characterized in that: Before searching the ordered original data set corresponding to the original data to be desensitized for target original data corresponding to the original data to be desensitized, the method further includes: Obtaining an ordered original data set corresponding to the original data to be desensitized, an ordered desensitized data set corresponding to the ordered original data set, and a set seed key provided by the data source; or From the designated data storage space, a preset ordered original data set corresponding to the original data to be desensitized, an ordered desensitized data set corresponding to the ordered original data set, and a set seed key are read.

10. The method according to any one of claims 1 to 8, characterized in that The number of data included in the original data set is greater than the number of data included in the ordered desensitized data set.

11. The method according to any one of claims 1 to 8, characterized in that The original data to be desensitized, the original data in the ordered original data set, and the desensitized data in the ordered desensitized data set are all numerical data.

12. A data publishing method, characterized in that: include: According to the call request initiated by the e-commerce platform, the original public domain data that the e-commerce platform needs to release to the merchant is obtained; Searching for target public domain data corresponding to the original public domain data in the ordered public domain data set corresponding to the original public domain data; Based on the set seed key, randomly sampling target desensitized data corresponding to the target public domain data from the ordered desensitized data set corresponding to the ordered public domain data set; The target desensitized data is returned to the e-commerce platform, so that the e-commerce platform can use the target desensitized data to replace the original public domain data and then publish it to merchants; wherein the public domain data in the ordered public domain data set are sorted by size, the desensitized data in the ordered desensitized data set are sorted by size, and the public domain data in the ordered public domain data set are mapped to the desensitized data in the ordered desensitized data set according to the size relationship, and the target desensitized data randomly sampled based on the seed key retains the orderliness between the original public domain data.

13. The method according to claim 12, characterized in that The public domain data includes at least one of industry sales amount, industry sales volume, category sales amount, category sales volume and platform visits within the e-commerce platform.

14. A data processing method, characterized in that: include: Obtain the original data to be desensitized generated during the operation; Searching for target original data corresponding to the original data to be desensitized in the ordered original data set corresponding to the original data to be desensitized; as well as Based on the set seed key, randomly sampling target desensitized data corresponding to the target original data in the ordered desensitized data set corresponding to the ordered original data set; Output the target desensitized data; wherein, the original data in the ordered original data set are sorted by size, the desensitized data in the ordered desensitized data set are sorted by size, and the original data in the ordered original data set are mapped to the desensitized data in the ordered desensitized data set according to the size relationship, and the target desensitized data randomly sampled based on the seed key retains the orderliness between the original data to be desensitized.

15. The method according to claim 14, characterized in that Obtain the original data to be desensitized during the operation, including: Display the desensitization configuration interface corresponding to the target application; In response to a first input operation of a user on the desensitization configuration interface, obtaining a data parameter input by the user pointing to the original data to be desensitized; According to the data parameters, the original data to be desensitized is obtained from the data set generated by the target application.

16. The method according to claim 15, characterized in that Also includes: In response to a second input operation of the user on the desensitization configuration interface, an ordered original data set corresponding to the original data to be desensitized, an ordered desensitized data set corresponding to the ordered original data set, and a set seed key input by the user are obtained.

17. A data publishing method, characterized in that: include: The e-commerce platform obtains the original public domain data that needs to be released to merchants; Searching for target public domain data corresponding to the original public domain data in the ordered public domain data set corresponding to the original public domain data; Based on the set seed key, randomly sampling target desensitized data corresponding to the target public domain data from the ordered desensitized data set corresponding to the ordered public domain data set; The target desensitized data is used to replace the original public domain data and then released to merchants; wherein the public domain data in the ordered public domain data set are sorted by size, the desensitized data in the ordered desensitized data set are sorted by size, and the public domain data in the ordered public domain data set are mapped to the desensitized data in the ordered desensitized data set according to the size relationship, and the target desensitized data randomly sampled based on the seed key retains the orderliness between the original public domain data.

18. A data processing device, characterized in that: include: Memory, processor, and communication components; The memory is used to store computer programs; The processor is coupled to the memory and is used to execute the computer program to: obtain the original data to be desensitized from the data source through the communication component according to a call request initiated by the data source; Searching for target original data corresponding to the original data to be desensitized in the ordered original data set corresponding to the original data to be desensitized; And based on the set seed key, randomly sampling target desensitized data corresponding to the target original data in the ordered desensitized data set corresponding to the ordered original data set; Return the target desensitized data to the data source end so that the data source end can output the target desensitized data; wherein, the original data in the ordered original data set are sorted by size, the desensitized data in the ordered desensitized data set are sorted by size, and the original data in the ordered original data set are mapped to the desensitized data in the ordered desensitized data set according to the size relationship, and the target desensitized data randomly sampled based on the seed key retains the orderliness between the original data to be desensitized.

19. A data processing device, characterized in that: include: Memory and processor; The memory is used to store computer programs; The processor is coupled to the memory and is used to execute the computer program to: obtain the original data to be desensitized generated by the data processing device during operation; Searching for target original data corresponding to the original data to be desensitized in the ordered original data set corresponding to the original data to be desensitized; And based on the set seed key, randomly sampling target desensitized data corresponding to the target original data in the ordered desensitized data set corresponding to the ordered original data set; Output the target desensitized data; wherein, the original data in the ordered original data set are sorted by size, the desensitized data in the ordered desensitized data set are sorted by size, and the original data in the ordered original data set are mapped to the desensitized data in the ordered desensitized data set according to the size relationship, and the target desensitized data randomly sampled based on the seed key retains the orderliness between the original data to be desensitized.

20. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor is caused to implement the steps in the method according to any one of claims 1 to 17.

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