Data processing method and device, equipment and storage medium

Abstract

The invention provides a data processing method and device, equipment and a storage medium, and the data processing method comprises the steps: responding to a privacy protection request for to-be-processed data, and constructing a target data tree corresponding to the to-be-processed data; performing data screening on the to-be-processed data by utilizing the target data tree to obtain to-be-protected data in the to-be-processed data; generating privacy budget data of the to-be-protected data according to a target privacy budget model and the to-be-protected data; and performing differential privacy protection processing on the to-be-protected data based on the privacy budget data to obtain target protection data. On the premise of ensuring data privacy, the data processing efficiency is improved, the situation that data practicability is greatly reduced due to data privacy protection is avoided, and then the data use experience of a user is improved.

Description

Technical Field

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

[0002] At present, with the rapid development of Internet technology and Internet of Things technology, more and more devices are connected to the network. These devices will generate massive real-time data during operation. While these real-time data can provide data support for various applications, they also bring serious risk of factor leakage. The existing privacy protection methods cannot effectively protect user privacy while ensuring the practicality of data, resulting in user privacy leakage. Summary of the invention

[0003] The embodiments of the present application provide a data processing method, apparatus, device and storage medium, which aim to solve the technical problem in the prior art that it is impossible to effectively protect user privacy while ensuring data practicality.

[0004] On the one hand, an embodiment of the present application provides a data processing method, the data processing method comprising the following steps:

[0005] In response to a privacy protection request for the data to be processed, construct a target data tree corresponding to the data to be processed;

[0006] Using the target data tree to screen the data to be processed, to obtain the data to be protected in the data to be processed;

[0007] Generate privacy budget data of the data to be protected according to the target privacy budget model and the data to be protected;

[0008] The data to be protected is subjected to differential privacy protection processing based on the privacy budget data to obtain target protected data.

[0009] In a possible implementation of the present application, the step of constructing a target data tree corresponding to the data to be processed includes:

[0010] Obtaining the pending data corresponding to the privacy protection request;

[0011] Counting the number of occurrences of each data to be processed to obtain the target data frequency corresponding to the data to be processed;

[0012] A target data tree corresponding to the data to be processed is constructed based on the target data frequency.

[0013] In a possible implementation of the present application, constructing a target data tree corresponding to the data to be processed based on the target data frequency includes:

[0014] Generate an initial data node according to the target data frequency and the data to be processed associated with the target data frequency;

[0015] Acquire a first data node and a second data node from the initial data nodes, and create a target parent node according to the first data node and the second data node;

[0016] A target data tree corresponding to the data to be processed is generated according to the target parent node, the first data node and the second data node.

[0017] In a possible implementation of the present application, the step of using the target data tree to screen the data to be processed to obtain the data to be protected in the data to be processed includes:

[0018] Parsing the target data tree to determine the target data level corresponding to each of the data to be processed;

[0019] If the target data level is greater than or equal to a preset level threshold, the to-be-processed data corresponding to the target data level is determined as the to-be-protected data.

[0020] In a possible implementation of the present application, generating privacy budget data of the data to be protected according to the target privacy budget model and the data to be protected includes:

[0021] Obtaining a data application scenario corresponding to the data to be protected and a privacy protection level corresponding to the data application scenario;

[0022] The privacy budget data of the data to be protected is generated according to the target privacy budget model and the privacy protection level.

[0023] In a possible implementation of the present application, generating privacy budget data of the data to be protected according to the target privacy budget model and the data to be protected includes:

[0024] Parsing the target data tree to obtain a target data level corresponding to the data to be protected in the target data tree;

[0025] Obtaining a privacy protection level corresponding to the data to be protected and a privacy hyperparameter corresponding to the privacy protection level;

[0026] The privacy budget data of the data to be protected is calculated according to the target privacy budget model, the target data level and the privacy hyperparameter.

[0027] In a possible implementation of the present application, performing differential privacy protection processing on the data to be protected based on the privacy budget data to obtain target protection data includes:

[0028] Determine target noise data according to the privacy protection level corresponding to the data to be protected and the privacy budget data;

[0029] The data to be protected is subjected to differential privacy protection processing according to the target noise data to obtain target protected data.

[0030] In another aspect, the present application provides a data processing device, the data processing device comprising:

[0031] A data modeling module is configured to respond to a privacy protection request for the data to be processed and construct a target data tree corresponding to the data to be processed;

[0032] A data screening module is configured to screen the data to be processed using the target data tree to obtain the data to be protected in the data to be processed;

[0033] A parameter generation module, configured to generate privacy budget data of the data to be protected according to a target privacy budget model and the data to be protected;

[0034] The data processing module is configured to perform differential privacy protection processing on the data to be protected based on the privacy budget data to obtain target protected data.

[0035] On the other hand, the present application further provides a data processing device, the data processing device comprising:

[0036] one or more processors;

[0037] Memory; and

[0038] One or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the processor to implement the steps of the data processing method.

[0039] On the other hand, the present application also provides a computer-readable storage medium on which a computer program is stored, and the computer program is loaded by a processor to execute the steps in the data processing method.

[0040] In this application, by responding to a privacy protection request for data to be processed, a target data tree corresponding to the data to be processed is constructed; the target data tree is used to screen the data to be processed to obtain the data to be protected in the data to be processed; the privacy budget data of the data to be protected is generated according to the target privacy budget model and the data to be protected; differential privacy protection is performed on the data to be protected based on the privacy budget data to obtain the target protected data. This is achieved by constructing a target data tree corresponding to the data to be processed, and screening the data to be protected in the data to be processed for privacy protection according to the target data tree, and dynamically generating appropriate privacy budget data according to the protected data using the target privacy budget model, and using the privacy budget data to perform differential privacy protection on the data to be protected, thereby improving data processing efficiency while ensuring data privacy, avoiding a significant reduction in data practicality due to privacy protection of the data, and thereby improving the user's data usage experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 A schematic diagram of a scenario of a data processing method according to an embodiment of the present application;

[0043] Figure 2 A flowchart of an embodiment of a data processing method in an embodiment of the present application;

[0044] Figure 3 A schematic diagram of a scenario of an embodiment of constructing a target data tree in a data processing method provided in an embodiment of the present application;

[0045] Figure 4 A flowchart of an embodiment of determining privacy budget data in a data processing method provided in an embodiment of the present application;

[0046] Figure 5 A flowchart of another embodiment of determining privacy budget data in the data processing method provided in an embodiment of the present application;

[0047] Figure 6 A schematic diagram of the structure of an embodiment of a data processing device provided in an embodiment of the present application;

[0048] Figure 7 A schematic diagram of the structure of an embodiment of a data processing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0050] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0051] In this application, the word "exemplary" is used to mean "serving as an example, illustration, or illustration." Any embodiment described in this application as "exemplary" is not necessarily to be construed as being preferred or advantageous over other embodiments. The following description is given to enable any person skilled in the art to implement and use the invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the invention can be implemented without using these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in this application.

[0052] At present, with the rapid development of Internet technology and Internet of Things technology, more and more devices are connected to the network. These devices will generate massive real-time data during operation. While these real-time data can provide data support for various applications, they also bring serious risk of factor leakage. The existing privacy protection methods cannot effectively protect user privacy while ensuring the practicality of data, resulting in user privacy leakage.

[0053] Based on this, the present application proposes a data processing method, device, equipment and computer-readable storage medium to solve the technical problem in the prior art of effectively protecting user privacy while ensuring data practicality.

[0054] The data processing method in the embodiment of the present invention is applied to a data processing device, which is arranged in a data processing equipment. The data processing equipment is provided with one or more processors, a memory, and one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement the data processing method; wherein the data processing equipment can be an intelligent terminal, such as a mobile phone, a tablet computer, a network device, and a smart computer; optionally, the data processing equipment can also be a server, or a service cluster composed of multiple servers.

[0055] like Figure 1 As shown, Figure 1 This is a scenario diagram of the data processing method of an embodiment of the present application. The data processing scenario in the embodiment of the present invention includes a data processing device 100 (a data processing device is integrated in the data processing device 100), and a computer-readable storage medium corresponding to the data processing method runs in the data processing device 100 to execute the steps of the data processing method.

[0056] Understandably, Figure 1 The data processing equipment in the data processing method scenario shown, or the devices included in the data processing equipment, does not constitute a limitation on the embodiments of the present invention, that is, the number of devices and types of devices of the data processing equipment included in the data processing method scenario, or the number of devices and types of devices included in each device, do not affect the overall implementation of the technical solution in the embodiments of the present invention, and can all be regarded as equivalent replacements or derivatives of the technical solution claimed to be protected in the embodiments of the present invention.

[0057] The data processing device 100 in the embodiment of the present invention is mainly used to: respond to a privacy protection request for data to be processed, and construct a target data tree corresponding to the data to be processed;

[0058] Using the target data tree to screen the data to be processed, to obtain the data to be protected in the data to be processed;

[0059] Generate privacy budget data of the data to be protected according to the target privacy budget model and the data to be protected;

[0060] The data to be protected is subjected to differential privacy protection processing based on the privacy budget data to obtain target protected data.

[0061] The data processing device 100 in the embodiment of the present invention can be an independent data processing device, such as a mobile phone, a tablet computer, a network device, a server, a smart terminal such as a smart computer, or a data processing network or a data processing cluster composed of multiple data processing devices.

[0062] The embodiments of the present application provide a data processing method, apparatus, device, and computer-readable storage medium, which are described in detail below.

[0063] It can be understood by those skilled in the art that Figure 1 The application environment shown in is only one of the application scenarios related to the present application solution and does not constitute a limitation on the application scenario of the present application solution. Other application environments may also include Figure 1 More or fewer data processing devices than shown, or data processing network connections, such as Figure 1 Only one data processing device is shown. It can be understood that the scenario of the data processing method can also include one or more data processing devices, which is not specifically limited here. The data processing device 100 can also include a memory for storing data to be processed and other data.

[0064] It should be noted that Figure 1 The scenario diagram of the data processing method shown is only an example. The scenario of the data processing method described in the embodiment of the present invention is to more clearly illustrate the technical solution of the embodiment of the present invention, and does not constitute a limitation on the technical solution provided by the embodiment of the present invention.

[0065] Based on the scenario of the above data processing method, various embodiments of the data processing method disclosed in the present invention are proposed.

[0066] like Figure 2 As shown, Figure 2 This is a flow chart of an embodiment of a data processing method in an embodiment of the present application. The data processing method includes the following steps 201 to 204:

[0067] 201. In response to a privacy protection request for data to be processed, construct a target data tree corresponding to the data to be processed;

[0068] The data processing method in this embodiment is applied to a data processing device. The type and number of data processing devices are not specifically limited. That is, the data processing device can be one or more smart terminals or servers that can perform dynamic differential privacy protection processing on data. In a specific embodiment, the data processing device is a smart computer.

[0069] Specifically, the data to be processed is real-time data uploaded by a data processing terminal, other IoT devices or other smart terminals, and is to be processed for privacy protection. Optionally, in a specific embodiment, the device to be processed is real-time IoT data, including real-time databases, log files, and terminal data uploaded by networked smart devices. These data to be processed can provide data support for various applications, but also increase the risk of privacy leakage for users. For example, in a specific embodiment, criminals can understand the lifestyle of residents by analyzing the real-time data of smart home appliances; for example, by monitoring the real-time data of a vehicle, the owner's current location can be known and his home address can be inferred, thereby carrying out malicious attacks. Therefore, such data to be processed needs to be processed for privacy protection.

[0070] Therefore, during operation, the data processing device also responds to a privacy protection request for the data to be processed, wherein the privacy protection request is an operation event that drives the data processing device to perform dynamic differential privacy protection processing on the data to be processed. Optionally, the triggering method of the privacy protection request is not specifically limited here, that is, the privacy protection request can be actively triggered by the user. For example, the user can obtain the data to be processed of a specified IoT device or other networked device through the data processing device, and actively trigger a privacy protection request for privacy protection of the specified data to be processed by clicking the privacy protection button on the data processing device. In addition, the privacy protection request can also be automatically triggered by the data processing device. For example, an automatic processing process is provided in the data processing device, and when the data to be processed is received, a privacy protection request for the data to be processed is automatically triggered.

[0071] Specifically, after obtaining the privacy protection request for the data to be processed, the data processing device also constructs a target data tree corresponding to the data to be processed. In a specific embodiment, the target data tree is a Huffman tree containing the statistical information of the frequency of occurrence of the data to be processed.

[0072] Specifically, after obtaining the data to be processed corresponding to the privacy protection request, the data processing device also performs frequency statistics on the data to be processed, and counts the number of occurrences of each data to be processed, so as to obtain the target data frequency corresponding to the data to be processed. And based on the target data frequency, a target data tree corresponding to the data to be processed is constructed. The target data frequency is a frequency parameter that characterizes the frequency of occurrence of the data to be processed.

[0073] Specifically, after obtaining each data to be processed and the target data frequency corresponding to the data to be processed, the data processing device also generates an initial data node according to the target data frequency and the data to be processed associated with the target data frequency. That is, the data processing device generates an initial data node for each data to be processed and the target data frequency associated with the data to be processed. The initial data node is a leaf node in the target data tree.

[0074] Specifically, after generating each initial data node, the data processing device further screens the initial data node according to the target data frequency, thereby obtaining a first data node with the smallest target data acquisition frequency among the initial data nodes, and a second data node with the smallest target data frequency except the first data node. The first data node is the initial data node with the smallest target data frequency among the initial data nodes. The second data node is the initial data node with the smallest target data frequency except the first data node among the initial data nodes.

[0075] Specifically, after acquiring the first data node and the second data node, the data processing device also creates a target parent node according to the first data node and the second data node. And generates a target data tree corresponding to the data to be processed according to the target parent node, the first data node and the second data node. That is, the data processing device merges the first data node and the second data node, takes the sum of the target data frequencies between the first data node and the second data node as the target data frequency of the target parent node, obtains the target parent node, and the first data node and the second data node serve as the left child node and the right child node of the target parent node.

[0076] Specifically, after generating the target parent node, the first data node and the second data node, the data processing device deletes the first data node and the second data node in the initial data node, and continues to obtain the new first data node and the new second data node in the initial data node according to the above steps, and merges the new first data node and the new second data node to obtain a new target parent node, until only one node is left in the initial data node, and the node is the target root node of the target data tree. At this point, the data processing device obtains the target data tree corresponding to the data to be processed.

[0077] Optional, such as Figure 3 As shown, Figure 3 A schematic diagram of a scenario of an embodiment of constructing a target data tree in a data processing method provided in an embodiment of the present application, wherein Figure 3In the embodiment shown, the data to be processed includes [138, 505, 187, 141, 121, 70], and the target data frequencies corresponding to the data to be processed are [5, 9, 12, 13, 16, 45] respectively. The data processing device constructs the target data tree obtained by the target data construction steps for the processed data as shown in FIG. Figure 3 As shown, the target root node is the level 1 in the target data hierarchy, the data to be processed

[70] is the level 2 in the target data hierarchy, and the corresponding target data frequency is

[45] . The data to be processed

[187] ,

[141] and

[121] are at the level 4 in the target data hierarchy, and the corresponding target data frequencies are

[12] ,

[13] and

[16] respectively. The data to be processed

[138] and

[505] are at the level 5 in the target data hierarchy, and the corresponding target data frequencies are [5] and [9] respectively. As shown in Table 1 below, Table 1 is the encoding table corresponding to the target data tree:

[0078]

[0079] Table 1

[0080] 202. Screen the data to be processed using the target data tree to obtain data to be protected in the data to be processed;

[0081] Specifically, in this embodiment, after constructing the target data tree corresponding to the data to be processed, the data processing device also uses the target data tree to perform data screening on the data to be processed, thereby obtaining the data to be protected in the data to be processed.

[0082] Specifically, the data processing device parses the target data tree to determine the target data level corresponding to each data to be processed in the target data tree. That is, the data processing device determines the target data level corresponding to each data to be processed in the target data tree according to the target data tree. The target data level is associated with the target data frequency corresponding to the data to be processed. The higher the target data frequency, the lower the corresponding target data level. Conversely, the lower the target data frequency, the higher the corresponding target data level.

[0083] Specifically, according to the Shannon information metric, data with lower frequency can contain more privacy information. Therefore, the data processing device is also pre-set with a preset hierarchical threshold for screening the data to be protected, so as to screen the low-frequency data to be protected in the data to be processed according to the preset hierarchical threshold, and perform differential privacy protection on the data to be protected in subsequent steps, so as to add more noise to the data to be protected to more effectively protect the privacy data of the data to be protected.

[0084] Specifically, after obtaining the target data level corresponding to the data to be processed, the data processing device also compares the target data level with the preset level threshold to determine whether the data to be processed corresponding to the target data level is protected data that requires privacy protection, thereby dynamically performing differential privacy protection on the data to be processed.

[0085] Optionally, if the target data level is less than the preset level threshold, the data processing device determines that the data to be processed corresponding to the target data level is high-frequency data containing less privacy information, and does not perform differential privacy protection on the data to be processed.

[0086] Optionally, if the target data level is greater than or equal to a preset level threshold, the data processing device determines that the data to be processed corresponding to the target data level is low-frequency data containing more privacy, and determines the data to be processed as data to be protected.

[0087] Optionally, in other embodiments, the data processing device can also dynamically adjust the privacy protection scale by adjusting the numerical value of the preset level threshold, thereby balancing the practicality of the data to be processed and the privacy protection efficiency.

[0088] Optionally, in other embodiments, in order to enhance the privacy protection, the data processing device can adjust the preset level threshold to 1, thereby achieving privacy protection for each data to be processed, thereby enhancing the data privacy protection.

[0089] 203. Generate privacy budget data of the data to be protected according to the target privacy budget model and the data to be protected;

[0090] Specifically, after determining the data to be protected that needs to be protected in the data to be processed, the data processing device also dynamically generates privacy budget data for the data to be protected based on the target privacy budget model and the data to be protected. The privacy budget data is a privacy parameter used to adjust the privacy amount of the data to be protected. The smaller the privacy budget data, the stronger the corresponding privacy protection, and the practicality of the corresponding data may be reduced. The larger the privacy budget data, the weaker the corresponding privacy protection, and the higher the practicality of the corresponding data. That is, the privacy budget data reflects the degree of privacy leakage of the data to be protected, and the privacy budget data determines the balance between the intensity of privacy protection and the practicality of data.

[0091] Optionally, in a specific embodiment, the data processing device can dynamically determine the privacy budget data of the data to be protected by using the target privacy budget model and the data application scenario corresponding to the data to be protected.

[0092] Optionally, in another specific embodiment, the data processing device can also dynamically calculate the privacy budget data of the data to be protected according to the target data level corresponding to the data to be protected and the target privacy budget model.

[0093] 204. Perform differential privacy protection processing on the data to be protected based on the privacy budget data to obtain target protected data.

[0094] Specifically, after obtaining the privacy budget data of the data to be protected, the data processing device also performs differential privacy protection on the data to be protected based on the privacy budget data to obtain the target protected data. Differential privacy protection is a privacy protection method that disturbs the original data by adding noise, thereby ensuring that the impact of a single record on the data when it is output is always below a certain threshold, so that attackers cannot use differential attacks to infer the sensitive information of individuals.

[0095] Specifically, after obtaining the privacy budget data of the data to be protected, the data processing device also determines the privacy protection level corresponding to the data to be protected, wherein the privacy protection level is an evaluation parameter that characterizes the data sensitivity or importance corresponding to the data to be protected. In a specific embodiment, the privacy protection level includes a strict privacy protection level, a medium privacy protection level, and a relaxed privacy protection level.

[0096] Specifically, after obtaining the privacy protection level and the privacy budget data, the data processing device calculates the noise scale parameter corresponding to the data to be protected according to the privacy protection level and the privacy budget data, wherein the noise scale parameter is a noise amplitude corresponding to the noise data to be added to the data to be protected. The larger the noise scale parameter, the higher the corresponding privacy protection degree.

[0097] Specifically, after obtaining the noise scale parameter, the data processing device also obtains the target noise data corresponding to the noise scale parameter, and performs differential privacy protection processing on the data to be protected according to the target noise data, that is, the data processing device randomly samples from the specified target noise distribution according to the noise scale parameter to obtain the target noise data corresponding to the data to be protected, adds the target noise data to the data to be protected for differential privacy protection processing, and obtains the target protected data. Optionally, in a specific embodiment, the target noise distribution can be any one of a Laplace noise distribution and an exponential noise distribution.

[0098] Optionally, in other embodiments, the data processing device can also separately obtain target noise data corresponding to the data to be processed according to the privacy budget data, and perform differential privacy protection processing on the data to be protected according to the noise data to obtain target protected data.

[0099] In this embodiment, the data processing device constructs a target data tree corresponding to the data to be processed by responding to a privacy protection request for the data to be processed; uses the target data tree to filter the data to be processed to obtain the data to be protected in the data to be processed; generates privacy budget data of the data to be protected according to the target privacy budget model and the data to be protected; performs differential privacy protection processing on the data to be protected based on the privacy budget data to obtain the target protected data. This is achieved by constructing a target data tree corresponding to the data to be processed, filtering the data to be protected in the data to be processed for privacy protection according to the target data tree, dynamically generating appropriate privacy budget data according to the protected data using the target privacy budget model, and performing differential privacy protection on the data to be protected using the privacy budget data, thereby improving data processing efficiency while ensuring data privacy, avoiding a significant reduction in data practicality due to privacy protection of the data, and thereby improving the user's data usage experience.

[0100] like Figure 4 As shown, Figure 4 This is a flow chart of an embodiment of determining privacy budget data in the data processing method provided in the embodiment of the present application. Specifically, in this embodiment, the data processing method further includes steps 301 to 302:

[0101] 301. Obtain a data application scenario corresponding to the data to be protected, and a privacy protection level corresponding to the data application scenario;

[0102] 302. Generate privacy budget data for the data to be protected according to the target privacy budget model and the privacy protection level.

[0103] Based on the above embodiments, in this embodiment, after determining the data to be protected, the data processing device also dynamically determines the privacy budget data of the data to be protected by using the target privacy budget model and the data application scenario corresponding to the data to be protected.

[0104] Specifically, after the data processing device screens out the data to be protected from the data to be processed, it also obtains the data application scenario corresponding to the data to be protected, and determines the privacy protection level corresponding to the data application scenario. That is, since different data application scenarios have different privacy leakage risks, the data processing device pre-sets corresponding privacy protection levels for different data application scenarios. In a specific embodiment, the privacy protection level includes a strict privacy protection level, a medium privacy protection level, and a relaxed privacy protection level.

[0105] Optionally, the data application scenario corresponding to the strict privacy protection level is a data application scenario that requires a high degree of privacy protection. For example, the data application scenario corresponding to the strict privacy protection level is a medical data scenario and a financial data scenario.

[0106] Optionally, the data application scenarios corresponding to the medium privacy protection level are data application scenarios corresponding to general privacy protection requirements. For example, the data application scenarios corresponding to the medium privacy protection level are user behavior analysis scenarios and market research scenarios.

[0107] Optionally, the data application scenario corresponding to the relaxed privacy protection level is a data application scenario with lower privacy protection requirements, for example, the data application scenario corresponding to the relaxed privacy protection level is statistical analysis of public data.

[0108] Specifically, after obtaining the data application scenario corresponding to the data to be protected, the data processing device matches the application scenario identifier of the data application scenario with the privacy scenario identifiers corresponding to each privacy protection level, thereby determining the privacy protection level corresponding to the data to be protected.

[0109] Specifically, after determining the privacy protection level of the data to be protected, the data processing device also generates privacy budget data of the data to be protected according to the target privacy budget model and the privacy protection level. That is, the target privacy budget model pre-stores candidate privacy budget data corresponding to each privacy protection level, and after obtaining the privacy protection level corresponding to the data to be protected, the privacy protection level is input into the target privacy budget model, so as to match the privacy budget data corresponding to the privacy protection level through the target privacy budget model.

[0110] Optionally, in a specific embodiment, the candidate privacy budget data corresponding to each privacy protection level stored in the target privacy budget are: the candidate privacy budget data corresponding to the strict privacy protection level is ε≈0.01 to 0.1; the candidate privacy budget data corresponding to the medium privacy protection level is ε≈0.1 to 1; the candidate privacy budget data corresponding to the loose privacy protection level is ε≈1 to 10. Among them, the smaller the privacy budget data, the stronger the corresponding privacy protection, and the practicality of the corresponding data may be reduced. The larger the privacy budget data, the weaker the corresponding privacy protection, and the higher the practicality of the corresponding data. That is, the privacy budget data reflects the degree of privacy leakage of the data to be protected, and the privacy budget data determines the balance between the strength of privacy protection and the practicality of data.

[0111] Specifically, after obtaining the privacy budget data of the data to be protected, the data processing device also performs differential privacy protection on the data to be protected based on the privacy budget data to obtain target protection data.

[0112] In this embodiment, the data processing device obtains the data application scenario corresponding to the data to be protected and the privacy protection level corresponding to the data application scenario; and generates the privacy budget data of the data to be protected according to the target privacy budget model and the privacy protection level. The corresponding privacy budget data is dynamically selected according to the data application scenario of the data to be protected, thereby balancing the practicality and privacy protection level of the data to be protected and optimizing the privacy protection effect.

[0113] like Figure 5 As shown, Figure 5 This is a flow chart of another embodiment of determining privacy budget data in the data processing method provided in the embodiment of the present application. Specifically, in this embodiment, the data processing method further includes steps 401 to 403:

[0114] 401. Parse the target data tree to obtain a target data level corresponding to the data to be protected in the target data tree;

[0115] 402. Obtain a privacy protection level corresponding to the data to be protected and a privacy hyperparameter corresponding to the privacy protection level;

[0116] 403. Calculate privacy budget data of the data to be protected according to the target privacy budget model, the target data level, and the privacy hyperparameter.

[0117] Based on the above embodiments, in this embodiment, after using the target data tree to filter the data to be protected in the data to be processed, the data processing device further determines the privacy budget data of the data to be protected according to the target data level of the data to be protected.

[0118] Specifically, the data processing device parses the target data tree to obtain the target data level corresponding to the data to be protected in the target data. The target data level is associated with the target data frequency corresponding to the data to be processed. The higher the target data frequency, the lower the corresponding target data level. Conversely, the lower the target data frequency, the higher the corresponding target data level.

[0119] Specifically, the data processing device further obtains the privacy protection level corresponding to the data to be protected, and the privacy hyperparameter corresponding to the privacy protection level. Among them, the privacy protection level includes a strict privacy protection level, a medium privacy protection level and a loose privacy protection level. Different privacy protection levels correspond to different privacy hyperparameters, wherein the privacy hyperparameter is a hyperparameter used to adjust the strength of privacy protection. In a specific embodiment, the value of the privacy hyperparameter can be 0.1, 1 and 10 according to different privacy protection levels. Among them, the higher the privacy protection level, the lower the corresponding privacy hyperparameter. That is, the value of the privacy hyperparameter corresponding to the strict privacy protection level is 0.1, the value of the privacy hyperparameter corresponding to the medium privacy protection level is 1, and the value of the privacy hyperparameter corresponding to the loose privacy protection level is 10.

[0120] Specifically, after obtaining the target data level and privacy hyperparameters, the data processing device also calculates the privacy budget data of the data to be protected according to the target privacy budget model, the target data level and the privacy hyperparameters. That is, the data processing device calculates the privacy budget data of the data to be protected by changing the privacy budget algorithm in the target privacy budget model. Specifically, the privacy budget algorithm formula is as follows:

[0121]

[0122] Among them, ε is the privacy budget data, e is a natural constant, approximately equal to 2.718281828459, level is the target data level, and β is the privacy hyperparameter.

[0123] Specifically, after obtaining the privacy budget data of the data to be protected, the data processing device also performs differential privacy protection on the data to be protected based on the privacy budget data to obtain target protection data.

[0124] In this embodiment, the data processing device parses the target data tree to obtain the target data level corresponding to the data to be protected in the target data tree; obtains the privacy protection level corresponding to the data to be protected, and the privacy hyperparameter corresponding to the privacy protection level; and calculates the privacy budget data of the data to be protected according to the target privacy budget model, the target data level and the privacy hyperparameter. The privacy budget data of the data to be protected is dynamically calculated through the target data level and the privacy protection level of the data to be protected, thereby dynamically determining the privacy protection strength of the data to be protected, effectively weighing the practicality and privacy protection degree of the data to be protected, and optimizing the privacy protection effect.

[0125] In order to better implement the data processing method in the embodiment of the present application, based on the data processing method, a data processing device is also provided in the embodiment of the present application, such as Figure 6 As shown, Figure 6The data processing device 500 is a schematic diagram of the structure of the data processing device provided in the embodiment of the present application. Specifically, the data processing device 500 includes:

[0126] The data modeling module 501 is configured to respond to a privacy protection request for the data to be processed and construct a target data tree corresponding to the data to be processed;

[0127] The data screening module 502 is configured to screen the data to be processed by using the target data tree to obtain the data to be protected in the data to be processed;

[0128] A parameter generation module 503, configured to generate privacy budget data of the data to be protected according to a target privacy budget model and the data to be protected;

[0129] The data processing module 504 is configured to perform differential privacy protection processing on the data to be protected based on the privacy budget data to obtain target protected data.

[0130] In a possible implementation of this embodiment, the data processing device constructs a target data tree corresponding to the data to be processed, including:

[0131] Obtaining the pending data corresponding to the privacy protection request;

[0132] Counting the number of occurrences of each data to be processed to obtain the target data frequency corresponding to the data to be processed;

[0133] A target data tree corresponding to the data to be processed is constructed based on the target data frequency.

[0134] In a possible implementation of this embodiment, the data processing device constructs a target data tree corresponding to the data to be processed based on the target data frequency, including:

[0135] Generate an initial data node according to the target data frequency and the data to be processed associated with the target data frequency;

[0136] Acquire a first data node and a second data node from the initial data nodes, and create a target parent node according to the first data node and the second data node;

[0137] A target data tree corresponding to the data to be processed is generated according to the target parent node, the first data node and the second data node.

[0138] In a possible implementation of this embodiment, the data processing device uses the target data tree to screen the data to be processed to obtain the data to be protected in the data to be processed, including:

[0139] Parsing the target data tree to determine the target data level corresponding to each of the data to be processed;

[0140] If the target data level is greater than or equal to a preset level threshold, the to-be-processed data corresponding to the target data level is determined as the to-be-protected data.

[0141] In a possible implementation of this embodiment, the data processing device generates privacy budget data of the data to be protected according to the target privacy budget model and the data to be protected, including:

[0142] Obtaining a data application scenario corresponding to the data to be protected and a privacy protection level corresponding to the data application scenario;

[0143] The privacy budget data of the data to be protected is generated according to the target privacy budget model and the privacy protection level.

[0144] In a possible implementation of this embodiment, the data processing device generates privacy budget data of the data to be protected according to the target privacy budget model and the data to be protected, including:

[0145] Parsing the target data tree to obtain a target data level corresponding to the data to be protected in the target data tree;

[0146] Obtaining a privacy protection level corresponding to the data to be protected and a privacy hyperparameter corresponding to the privacy protection level;

[0147] The privacy budget data of the data to be protected is calculated according to the target privacy budget model, the target data level and the privacy hyperparameter.

[0148] In a possible implementation of this embodiment, the data processing device performs differential privacy protection processing on the data to be protected based on the privacy budget data to obtain target protection data, including:

[0149] Determine target noise data according to the privacy protection level corresponding to the data to be protected and the privacy budget data;

[0150] The data to be protected is subjected to differential privacy protection processing according to the target noise data to obtain target protected data.

[0151] In this embodiment, the data processing device constructs a target data tree corresponding to the data to be processed by responding to a privacy protection request for the data to be processed; uses the target data tree to filter the data to be processed to obtain the data to be protected in the data to be processed; generates privacy budget data of the data to be protected according to the target privacy budget model and the data to be protected; performs differential privacy protection processing on the data to be protected based on the privacy budget data to obtain the target protected data. This is achieved by constructing a target data tree corresponding to the data to be processed, filtering the data to be protected in the data to be processed for privacy protection according to the target data tree, dynamically generating appropriate privacy budget data according to the protected data using the target privacy budget model, and performing differential privacy protection on the data to be protected using the privacy budget data, thereby improving data processing efficiency while ensuring data privacy, avoiding a significant reduction in data practicality due to privacy protection of the data, and thus improving the user's data usage experience.

[0152] The embodiment of the present invention also provides a data processing device, such as Figure 7 As shown, Figure 7 A schematic diagram of the structure of an embodiment of a data processing device provided in an embodiment of the present application.

[0153] The data processing device integrates any one of the data processing devices provided in the embodiments of the present invention, and the data processing device includes:

[0154] one or more processors;

[0155] Memory; and

[0156] One or more applications, wherein the one or more applications are stored in the memory and are configured so that the processor executes the steps of the data processing method described in any of the above data processing method embodiments.

[0157] Specifically, the data processing device may include one or more processing core processors 601, one or more computer-readable storage media memories 602, a power supply 603, an input unit 604 and other components. Those skilled in the art will understand that Figure 7 The data processing device structure shown in the figure does not constitute a limitation on the data processing device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. Among them:

[0158] The processor 601 is the control center of the data processing device. It uses various interfaces and lines to connect various parts of the entire data processing device. By running or executing software programs and / or modules stored in the memory 602, and calling data stored in the memory 602, it executes various functions of the data processing device and processes data, thereby monitoring the data processing device as a whole. Optionally, the processor 601 may include one or more processing cores; preferably, the processor 601 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 601.

[0159] The memory 602 can be used to store software programs and modules. The processor 601 executes various functional applications and data processing by running the software programs and modules stored in the memory 602. The memory 602 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the data processing device, etc. In addition, the memory 602 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory 602 may also include a memory controller to provide the processor 601 with access to the memory 602.

[0160] The data processing device also includes a power supply 603 for supplying power to each component. Preferably, the power supply 603 can be logically connected to the processor 601 through a power management system, so as to manage charging, discharging, power consumption and other functions through the power management system. The power supply 603 can also include one or more DC or AC power supplies, recharging systems, power failure detection circuits, power converters or inverters, power status indicators and other arbitrary components.

[0161] The data processing device may further include an input unit 604, which may be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control.

[0162] Although not shown, the data processing device may further include a display unit, etc., which will not be described in detail herein. Specifically, in this embodiment, the processor 601 in the data processing device will load the executable files corresponding to the processes of one or more application programs into the memory 602 according to the following instructions, and the processor 601 will run the application programs stored in the memory 602, thereby realizing various functions, as follows:

[0163] In response to a privacy protection request for the data to be processed, construct a target data tree corresponding to the data to be processed;

[0164] Using the target data tree to screen the data to be processed, to obtain the data to be protected in the data to be processed;

[0165] Generate privacy budget data of the data to be protected according to the target privacy budget model and the data to be protected;

[0166] The data to be protected is subjected to differential privacy protection processing based on the privacy budget data to obtain target protected data.

[0167] To this end, an embodiment of the present invention provides a computer-readable storage medium, which may include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc. A computer program is stored thereon, and the computer program is loaded by a processor to execute the steps in any data processing method provided in an embodiment of the present invention. For example, the computer program may be loaded by a processor to execute the following steps:

[0168] In response to a privacy protection request for the data to be processed, construct a target data tree corresponding to the data to be processed;

[0169] Using the target data tree to screen the data to be processed, to obtain the data to be protected in the data to be processed;

[0170] Generate privacy budget data of the data to be protected according to the target privacy budget model and the data to be protected;

[0171] The data to be protected is subjected to differential privacy protection processing based on the privacy budget data to obtain target protected data.

[0172] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the detailed description of other embodiments above, and will not be repeated here.

[0173] In specific implementation, the above units or structures can be implemented as independent entities, or can be arbitrarily combined to be implemented as the same or several entities. The specific implementation of the above units or structures can refer to the previous method embodiments, which will not be repeated here.

[0174] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.

[0175] The above is a detailed introduction to a data processing method provided in an embodiment of the present application. Specific embodiments are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A data processing method, characterized in that: The data processing method comprises: In response to a privacy protection request for the data to be processed, construct a target data tree corresponding to the data to be processed; Using the target data tree to screen the data to be processed, to obtain the data to be protected in the data to be processed; Generate privacy budget data of the data to be protected according to the target privacy budget model and the data to be protected; The data to be protected is subjected to differential privacy protection processing based on the privacy budget data to obtain target protected data.

2. The data processing method according to claim 1, characterized in that: The step of constructing a target data tree corresponding to the data to be processed includes: Obtaining the pending data corresponding to the privacy protection request; Counting the number of occurrences of each data to be processed to obtain the target data frequency corresponding to the data to be processed; A target data tree corresponding to the data to be processed is constructed based on the target data frequency.

3. The data processing method according to claim 2, characterized in that: The step of constructing a target data tree corresponding to the data to be processed based on the target data frequency includes: Generate an initial data node according to the target data frequency and the data to be processed associated with the target data frequency; Acquire a first data node and a second data node from the initial data nodes, and create a target parent node according to the first data node and the second data node; A target data tree corresponding to the data to be processed is generated according to the target parent node, the first data node and the second data node.

4. The data processing method according to claim 1, characterized in that: The using the target data tree to screen the data to be processed to obtain the data to be protected in the data to be processed includes: Parsing the target data tree to determine the target data level corresponding to each of the data to be processed; If the target data level is greater than or equal to a preset level threshold, the to-be-processed data corresponding to the target data level is determined as the to-be-protected data.

5. The data processing method according to any one of claims 1 to 4, characterized in that: The step of generating privacy budget data of the data to be protected according to the target privacy budget model and the data to be protected includes: Obtaining a data application scenario corresponding to the data to be protected and a privacy protection level corresponding to the data application scenario; The privacy budget data of the data to be protected is generated according to the target privacy budget model and the privacy protection level.

6. The data processing method according to any one of claims 1 to 4, characterized in that: The step of generating privacy budget data of the data to be protected according to the target privacy budget model and the data to be protected includes: Parsing the target data tree to obtain a target data level corresponding to the data to be protected in the target data tree; Obtaining a privacy protection level corresponding to the data to be protected and a privacy hyperparameter corresponding to the privacy protection level; The privacy budget data of the data to be protected is calculated according to the target privacy budget model, the target data level and the privacy hyperparameter.

7. The data processing method according to claim 6, characterized in that: The performing differential privacy protection processing on the data to be protected based on the privacy budget data to obtain target protected data includes: Determine target noise data according to the privacy protection level corresponding to the data to be protected and the privacy budget data; The data to be protected is subjected to differential privacy protection processing according to the target noise data to obtain target protected data.

8. A data processing device, characterized in that: The data processing device comprises: A data modeling module is configured to respond to a privacy protection request for the data to be processed and construct a target data tree corresponding to the data to be processed; A data screening module is configured to screen the data to be processed using the target data tree to obtain the data to be protected in the data to be processed; A parameter generation module, configured to generate privacy budget data of the data to be protected according to a target privacy budget model and the data to be protected; The data processing module is configured to perform differential privacy protection processing on the data to be protected based on the privacy budget data to obtain target protected data.

9. A data processing device, characterized in that: The data processing device comprises: one or more processors; Memory; and One or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the processor to implement the steps of the data processing method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and the computer program is loaded by a processor to execute the steps of the data processing method according to any one of claims 1 to 7.

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