Business data desensitization method and device, storage medium and computer equipment

Abstract

The embodiment of the invention discloses a business data desensitization method and device, a storage medium and computer equipment, and relates to the field of data processing. The method comprises the following steps: acquiring a data set; the data set comprises multiple pieces of business data of the enterprise; identifying a frequent sequence of which the support degree is greater than a support degree threshold value in the data set, and marking a frequent sequence in each piece of business data; selecting a mapping table; the mapping table is used for storing a mapping relation between the identifier and the frequent sequence; and according to the mapping table, replacing the frequent sequence included in each piece of service data with an identifier to obtain a desensitized data set. Through a series of efficient and accurate steps, efficient desensitization processing of enterprise business data is realized. The data desensitization efficiency is improved, the processing cost is reduced, the accuracy of the desensitization result is ensured, and sensitive information of enterprises is protected.

Description

Technical Field

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

[0002] As the scale of business continues to expand, the amount of business data generated by its business system has shown an explosive growth trend. This trend hides huge information security challenges. In this vast ocean of data, there are highly sensitive and confidential data related to the core competitiveness of the enterprise, such as commodity transaction records, inventory flow information, financing and loan details. In order to ensure that this information is not threatened by unauthorized access or leakage, it is particularly important to effectively desensitize such high-risk data.

[0003] However, the current desensitization strategy mainly relies on manual operations by data analysts: first, they rely on their personal experience and expertise to identify high-risk items from massive amounts of data, and then implement desensitization measures one by one. This traditional process is not only time-consuming and labor-intensive, but also inefficient, and has significant limitations in terms of identification accuracy, because it is difficult to fully and accurately cover all potential high-risk points with manual judgment alone.

[0004] Therefore, faced with the surge in data volume and the upgrade of data security needs, the existing desensitizing processing methods can no longer meet the requirements of efficient and accurate management, and a more intelligent and automated solution is urgently needed to meet this challenge. Summary of the invention

[0005] The embodiments of the present application provide a method, device, storage medium and computer equipment for desensitizing business data, which can solve the problems of low efficiency and high risk of data desensitization processing in the prior art. The technical solution is as follows:

[0006] In a first aspect, an embodiment of the present application provides a method for desensitizing business data, the method comprising:

[0007] Acquire a data set; the data set includes multiple business data of the enterprise;

[0008] Identifying frequent sequences whose support is greater than a support threshold in the data set, and marking frequent sequences in each piece of business data;

[0009] Selecting a mapping table; the mapping table is used to store a mapping relationship between an identifier and a frequent sequence;

[0010] According to the mapping table, the frequent sequences included in each piece of business data are replaced with an identifier to obtain a desensitized data set.

[0011] In a second aspect, an embodiment of the present application provides a device for desensitizing business data, the device comprising:

[0012] An acquisition unit, used to acquire a data set; the data set includes a plurality of business data of an enterprise;

[0013] A marking unit, used to identify frequent sequences whose support is greater than a support threshold in the data set, and mark the frequent sequences in each piece of business data;

[0014] A selection unit, used to select a mapping table; the mapping table is used to store a mapping relationship between an identifier and a frequent sequence;

[0015] The conversion unit is used to replace the frequent sequence included in each business data with an identifier according to the mapping table to obtain a desensitized data set.

[0016] In a third aspect, an embodiment of the present application provides a computer storage medium, wherein the computer storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the above-mentioned method steps.

[0017] In a fourth aspect, an embodiment of the present application provides a computer device, which may include: a processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the above-mentioned method steps.

[0018] The beneficial effects brought about by the technical solutions provided by some embodiments of the present application include at least:

[0019] By identifying frequent sequences in the data set whose support is greater than the support threshold, the scope of data that needs to be desensitized is effectively narrowed. This step uses frequent item set mining technology to quickly locate sensitive information with a high frequency in the data set, thereby avoiding unnecessary data processing overhead. After identifying the frequent sequences, the present application uses a mapping table to store the mapping relationship between identifiers and frequent sequences. This storage method not only facilitates the rapid search and replacement of frequent sequences, but also greatly reduces memory usage and improves the efficiency of data processing. According to the mapping table, the present solution replaces the frequent sequences included in each business data with an identifier to generate a desensitized data set. This step achieves rapid desensitization of a large amount of data by batch replacement, significantly improving the efficiency of data desensitization. When identifying frequent sequences, the present application uses a support threshold as a judgment criterion. This criterion can ensure that only those sensitive information that is truly representative and appears frequently is identified, thereby avoiding misjudgment and missed judgment. When constructing the mapping table, the scheme assigns a unique identifier to each frequent sequence. This one-to-one mapping relationship ensures that during the desensitization process, each frequent sequence can be accurately replaced with the corresponding identifier, thus avoiding data confusion and erroneous replacement problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 It is a schematic diagram of the network architecture provided by the embodiment of the present application;

[0022] Figure 2 It is a flowchart of a method for desensitizing business data provided in an embodiment of the present application;

[0023] Figure 3 It is a structural schematic diagram of a business data desensitization device provided by this application;

[0024] Figure 4 It is a structural schematic diagram of a computer device provided by this application. DETAILED DESCRIPTION

[0025] In order to make the objectives, technical solutions and advantages of the present application more clear, the embodiments of the present application will be further described in detail below with reference to the accompanying drawings.

[0026] It should be noted that the business data desensitization method provided in the present application is generally executed by a computer device, and correspondingly, the business data desensitization device is generally set in the computer device.

[0027] Figure 1 An exemplary system architecture of a business data desensitization method or a business data desensitization device that can be applied to the present application is shown.

[0028] like Figure 1 As shown, the system architecture may include: a computer device 101 and a server 102. The computer device 101 and the server 102 may communicate via a network, and the network is used as a medium for providing communication links between the above-mentioned units. The network may include various types of wired communication links or wireless communication links, for example: a wired communication link includes an optical fiber, a twisted pair or a coaxial cable, etc., and a wireless communication link includes a Bluetooth communication link, a wireless fidelity (WIreless-FIdelity, Wi-Fi) communication link or a microwave communication link, etc.

[0029] A data warehouse is deployed in the server 102 , and the computer device 101 obtains the required data set in the data warehouse, and then decrypts the business data in the data set.

[0030] It should be noted that the computer device 101 and the server 102 can be hardware or software. When the computer device 101 and the server 102 are hardware, they can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When the computer device 101 and the server 102 are software, they can be implemented as multiple software or software modules (for example, for providing distributed services), or as a single software or software module, which is not specifically limited here.

[0031] Various communication client applications can be installed on the computer device of the present application, such as: video recording applications, video playback applications, voice interaction applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0032] The computer device may be hardware or software. When the computer device is hardware, it may be various computer devices with a display screen, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers. When the computer device is software, it may be installed in the computer devices listed above. It may be implemented as multiple software or software modules (for example, to provide distributed services), or it may be implemented as a single software or software module, which is not specifically limited here.

[0033] When the computer device is hardware, a display device and a camera may be installed thereon. The display device may be any device capable of realizing a display function, and the camera is used to collect video streams; for example, the display device may be a cathode ray tube display (CR), a light-emitting diode display (LED), an electronic ink screen, a liquid crystal display (LCD), a plasma display panel (PDP), etc. The user may use the display device on the computer device to view displayed text, pictures, videos and other information.

[0034] It should be understood that Figure 1 The number of computer devices, networks and servers in the embodiment is only for illustration. Any number of computer devices, networks and servers may be used as required.

[0035] The following will be combined with the attached Figure 2 , the desensitization method of the business data provided in the embodiment of the present application is described in detail. Among them, the desensitization device for the business data in the embodiment of the present application can be Figure 1 Computer equipment shown.

[0036] See also Figure 2 , which is a flow chart of a method for desensitizing business data provided by the present application embodiment. Figure 2 As shown, the method of the embodiment of the present application may include the following steps:

[0037] S201. Obtain a data set; the data set includes multiple business data of the enterprise.

[0038] Among them, the computer device obtains the data set from a remote server, database or third-party data provider through a network interface (such as HTTP, FTP, API, etc.). The data set may be stored in a variety of formats, such as CSV, Excel, JSON, database files (such as SQL files), etc. According to the format of the data set, the computer device selects a suitable parsing library or tool to read and parse the data. The data is loaded into a data structure in memory (such as a list, dictionary, database table, etc.) for subsequent processing. If the data set is large, the computer device may use block reading or streaming processing to reduce memory usage. After the data is loaded, the computer device preprocesses the data, including removing null values, outliers, duplicate values, etc. The data is converted into a format suitable for subsequent analysis, such as converting text data into numerical data (such as word frequency, TF-IDF, etc.). The data set is usually organized in a table form, containing multiple business data, each of which contains multiple fields (such as order ID, customer ID, product ID, transaction time, etc.). It should be noted that the field types included in each business data may be different.

[0039] In some embodiments of the present application, a computer device sends a data request to a remote server, the data request carries a query condition, and the remote server returns a corresponding data set to the computer device according to the query condition in the data request.

[0040] Therein, the computer device prepares data requests according to business requirements and data characteristics.

[0041] The data request may contain query conditions, such as time range, region, business type, etc., so that the remote server can return a data set that meets the conditions. The computer device sends a data request to the remote server through the network interface. The data request is encapsulated in a specific format and contains necessary authentication information and query conditions. The remote server retrieves the data that meets the conditions in the database based on the query conditions in the data request. The remote server encapsulates the retrieved data set in a specific format (such as CSV, Excel, JSON, etc.) and returns it to the computer device through the network interface. After receiving the data set, the computer device selects a suitable parsing library or tool to read and parse the data according to the format of the data set. The data is loaded into a data structure in memory (such as a list, dictionary, database table, etc.) for subsequent processing. If the data set is large, the computer device may use block reading or streaming processing to reduce memory usage.

[0042] S202: identifying frequent sequences whose support is greater than a support threshold in the data set, and marking the frequent sequences in each piece of business data.

[0043] Among them, the computer device selects a suitable frequent sequence mining algorithm, such as Apriori, PrefixSpan, GSP, etc. The choice of algorithm depends on the characteristics of the data, the mining objectives, and the limitations of computing resources. According to business requirements and data characteristics, the computer device sets a support threshold. The support threshold can determine which frequent sequences will be identified. The computer device runs the frequent sequence mining algorithm to mine frequent sequences with support greater than the support threshold in the data set. The algorithm identifies frequent sequences by traversing the data set, calculating the support of the item set, and generating candidate item sets. The computer device traverses each business data, checks and marks the part containing the frequent sequence. Marking can be achieved by inserting special tags in the data, using indexes or pointers, etc., so that it can be quickly identified during subsequent processing.

[0044] Specifically, a set of special marking rules for marking frequent sequences is defined based on business requirements and data characteristics. These rules may include specific symbols, codes, or identifiers that are used to uniquely identify frequent sequences in business data. Use data mining algorithms to identify frequent sequences in business data. Frequent sequences are marked in the data set according to the defined marking rules. Marking can be done by directly adding a new field to the data record, or by modifying the value of an existing field to include a special tag. The marked data is verified to ensure that the frequent sequences are correctly marked. Sampling inspection, data comparison, and other methods can be used to verify the accuracy of the marking.

[0045] Based on the marking of frequent sequences, an index is created for the business data. The index can be built based on data structures such as hash tables, B-trees, red-black trees, etc. to improve query efficiency. When the business data changes, the index is updated in a timely manner to reflect the new frequent sequences. This may include adding new index items, deleting outdated index items, or updating the values ​​of existing index items. In subsequent data analysis or data mining tasks, the index can be used to quickly locate data records containing frequent sequences. This can significantly improve query efficiency and reduce processing time.

[0046] For each marked frequent sequence, a unique pointer is assigned. The pointer can be stored in the index to point to the location of the data record containing the frequent sequence. A pointer table or pointer array is maintained to track and manage all assigned pointers. The uniqueness and validity of the pointers are ensured to avoid pointer conflicts or invalidation. When it is necessary to access the data record containing the frequent sequence, the target location can be quickly located through the pointer. This can simplify the data access process and improve the flexibility of data processing.

[0047] S203: Select a mapping table.

[0048] Among them, the computer device designs a mapping table structure for storing the mapping relationship between frequent sequences and unique identifiers. The mapping table can be implemented using data structures such as hash tables, dictionaries, and database tables. For each frequent sequence mined, the computer device generates a unique identifier (such as UUID, hash value, etc.). The unique identifier is used to replace the frequent sequence during the desensitization process. The computer device stores the mapping relationship between the frequent sequence and the unique identifier in the mapping table. The mapping table is loaded into the memory or stored on an external storage device for subsequent quick access.

[0049] Specifically, when the mapping table is a hash table, the computer device initializes a hash table to store the mapping relationship between frequent sequences and identifiers. The hash table is usually composed of a key-value pair, where the key is the frequent sequence and the value is the corresponding identifier. For each mined frequent sequence, the computer device generates a unique identifier. This identifier can be a UUID, a hash value or other unique identifier. The frequent sequence is used as the key and the generated identifier is used as the value to store it in the hash table. In order to efficiently store and retrieve data, the computer device needs to design a suitable hash function. The hash function converts the frequent sequence into a hash value, which will be used to determine the storage location of the frequent sequence in the hash table. A good hash function should minimize the possibility of hash collisions (i.e., different frequent sequences produce the same hash value) and ensure that the load factor of the hash table (i.e., the ratio of the number of filled slots in the hash table to the total number of slots) remains within a reasonable range. According to the size of the data set and the distribution of the frequent sequences, the computer device can adjust the size of the hash table (i.e., the number of slots) and the parameters of the hash function to optimize the performance and space utilization of the hash table.

[0050] In some embodiments of the present application, the computer device randomly selects a mapping table from a preset mapping table set according to a business scenario.

[0051] Among them, according to business requirements and data characteristics, multiple mapping tables are constructed, each corresponding to a different data subset or business scenario. Each mapping table follows the same structure, associating frequent sequences with unique identifiers.

[0052] For each mapping table, a dynamic update mechanism is set up to monitor and process new frequent sequences or changes in existing frequent sequences. The update mechanism may be triggered based on time intervals, data changes, or business needs.

[0053] When data masking needs to be processed, the computer device first determines the current data subset or business scenario. Based on the determined data subset or business scenario, a mapping table is randomly selected from the mapping table set to process the data masking. Random selection can be implemented through a variety of algorithms, such as random number generators, shuffling algorithms, etc. The randomly selected mapping table is used to replace the frequent sequences in the data set. The replacement process follows the correspondence between the frequent sequences in the mapping table and the unique identifier. A consistency check is performed on each mapping table regularly to ensure that the mapping relationship is correct. Based on the size and access frequency of the mapping table, performance optimization measures are taken to speed up access.

[0054] S204: According to the mapping table, the frequent sequence included in each piece of business data is replaced with an identifier to obtain a desensitized data set.

[0055] Among them, the computer device traverses each business data, searches for and identifies the part containing frequent sequences. For each frequent sequence identified, the computer device searches for the corresponding unique identifier in the mapping table. The computer device replaces the frequent sequence with the corresponding unique identifier. The replacement operation can be implemented by string replacement, database update, etc. The replaced business data is combined into a new desensitized data set. The desensitized data set retains the structure and semantics of the original data, but the sensitive information has been replaced with a unique identifier. The computer device stores the desensitized data set on a local storage device or a remote server. According to business needs, the computer device may also need to back up and restore the desensitized data set.

[0056] In some embodiments of the present application, a support threshold is set and a mapping table is created based on a configuration operation of a user.

[0057] The computer device provides a user interaction interface (such as a GUI or CLI) to allow the user to input or select a support threshold. The user enters a specific value as the support threshold through the interactive interface, or selects a suitable threshold from the preset options. The computer device receives the user's input and sets the value as the support threshold in the subsequent frequent sequence mining process. The computer device may store the support threshold in a configuration file so that it can be automatically loaded during subsequent operations. After the setting is completed, the computer device can perform a simple verification to ensure that the support threshold is a valid value.

[0058] The user may wish to customize the mapping table creation process, such as selecting a specific hash function, setting the size of the hash table, etc. The computer device provides an option that allows the user to specify these parameters or choose from preset configurations. If the user does not provide specific configuration information, the computer device will use default parameters and algorithms to create the hash table. This may include selecting a default hash function, estimating the size of the hash table based on the size of the data set, etc. After the hash table is created, the computer device will initialize the mapping relationship. This usually means reserving space for the frequent sequences to be mined and preparing to associate them with the generated identifiers. The hash table (mapping table) is stored in the memory of the computer device for fast access during frequent sequence mining and data desensitization. As the mining process proceeds, the computer device will continuously update the hash table, adding new frequent sequences and corresponding identifiers.

[0059] The present application specifically includes the following beneficial effects:

[0060] By identifying and replacing frequent sequences in business data, this technical solution can effectively desensitize sensitive information. This helps prevent data leakage and protect the company's business secrets and customer privacy. When the desensitized data set is used for analysis, testing or sharing, the risk of data abuse or misuse is reduced because sensitive information has been removed.

[0061] Using a mapping table to store the mapping relationship between identifiers and frequent sequences can greatly speed up data desensitization. Because the mapping table provides a fast search and replace function, it avoids the tedious process of searching and replacing frequent sequences one by one in each business data. This application retains the structure and integrity of the business data during the desensitization process, and only replaces sensitive information, thereby ensuring the consistency and availability of the data.

[0062] This application allows users to set support thresholds and select the type of mapping table (such as hash table) according to actual needs, which provides great flexibility. Users can adjust these parameters according to the characteristics of the data and the requirements of desensitization to achieve the best processing effect. As the business data of an enterprise grows and changes, this technical solution can be easily expanded to adapt to new data scales and types. For example, new frequent sequence mining algorithms can be added, the storage structure of the mapping table can be optimized, etc.

[0063] The following is an embodiment of the device of the present application, which can be used to execute the embodiment of the method of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the method of the present application.

[0064] See also Figure 3, which shows a schematic diagram of the structure of a business data desensitization device provided by an exemplary embodiment of the present application, hereinafter referred to as device 3. The device 3 can be implemented as all or part of a computer device through software, hardware, or a combination of both. The device 34 includes: an acquisition unit 301, a marking unit 302, a selection unit 303, and a conversion unit 304.

[0065] The acquisition unit 301 is used to acquire a data set; the data set includes multiple business data of the enterprise;

[0066] A marking unit 302, configured to identify frequent sequences whose support is greater than a support threshold in the data set, and mark frequent sequences in each piece of business data;

[0067] A selection unit 303 is used to select a mapping table; the mapping table is used to store the mapping relationship between the identifier and the frequent sequence;

[0068] The conversion unit 304 is used to replace the frequent sequence included in each piece of business data with an identifier according to the mapping table to obtain a desensitized data set.

[0069] In one or more possible embodiments, it further includes:

[0070] The cleaning unit is used to clean each piece of business data in the data set, wherein the data cleaning includes: removing null values, abnormal values, and duplicate values ​​in the business data.

[0071] In one or more possible embodiments, acquiring a data set includes:

[0072] Sending a data request from a remote server via a network interface, wherein the data request carries a query condition;

[0073] A data set returned by the remote server based on the query condition is received.

[0074] In one or more possible embodiments, the mapping table is a hash table or a dictionary.

[0075] In one or more possible embodiments, marking a frequent sequence in each piece of business data includes:

[0076] Frequent sequences are marked in business data through special tags, indexes, or pointers.

[0077] In one or more possible embodiments, the selection mapping table includes:

[0078] Select a mapping table from the preset mapping table set based on the business scenario.

[0079] In one or more possible embodiments, it further includes:

[0080] The setting unit is used to set the support threshold and create a mapping table based on the configuration operation of the user.

[0081] It should be noted that when the device 3 provided in the above embodiment executes the method for desensitizing business data, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the above functions. In addition, the desensitizing device for business data provided in the above embodiment and the desensitizing method embodiment for business data belong to the same concept. The implementation process is detailed in the method embodiment, which will not be repeated here.

[0082] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0083] The present application also provides a computer storage medium, which can store multiple instructions, and the instructions are suitable for being loaded and executed by a processor as described above. Figure 2 The method steps of the embodiment shown in the figure can be found in the specific implementation process. Figure 2 The specific description of the illustrated embodiment will not be repeated here.

[0084] The present application also provides a computer program product, which stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the business data desensitization method described in the above embodiments.

[0085] See also Figure 4 , which is a schematic diagram of the structure of a computer device according to an embodiment of the present application. Figure 4 As shown, the computer device 400 may include: at least one processor 401 , at least one network interface 404 , a user interface 403 , a memory 405 , and at least one communication bus 402 .

[0086] The communication bus 402 is used to realize the connection and communication between these components.

[0087] The user interface 403 may include a display screen (Display) and a camera (Camera), and the optional user interface 403 may also include a standard wired interface and a wireless interface.

[0088] The network interface 404 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0089] Among them, the processor 401 may include one or more processing cores. The processor 401 uses various interfaces and lines to connect various parts within the entire computer device 400, and executes various functions and processes data of the computer device 400 by running or executing instructions, programs, code sets or instruction sets stored in the memory 405, and calling data stored in the memory 405. Optionally, the processor 401 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 401 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 401, and it can be implemented separately through a chip.

[0090] Among them, the memory 405 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 405 includes a non-transitory computer-readable storage medium. The memory 405 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 405 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 405 may optionally be at least one storage device located away from the aforementioned processor 401. As Figure 4 As shown, the memory 405 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program.

[0091] exist Figure 4In the computer device 400 shown in FIG. 1 , the user interface 403 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 401 can be used to call the application program stored in the memory 405 and specifically execute the following steps: Figure 2 The specific process can be referred to Figure 2 As shown, no further description is given here.

[0092] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0093] The above disclosure is only the preferred embodiment of the present application, which certainly cannot be used to limit the scope of rights of the present application. Therefore, equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. A method for desensitizing business data, characterized in that: include: Get the dataset; The data set includes multiple business data of the enterprise; Identifying frequent sequences whose support is greater than a support threshold in the data set, and marking frequent sequences in each piece of business data; Select the mapping table; The mapping table is used to store the mapping relationship between the identifier and the frequent sequence; According to the mapping table, the frequent sequences included in each piece of business data are replaced with an identifier to obtain a desensitized data set.

2. The method according to claim 1, characterized in that Also includes: Data cleaning is performed on each piece of business data in the data set, and the data cleaning includes: removing null values, abnormal values, and duplicate values ​​in the business data.

3. The method according to claim 1 or 2, characterized in that: The acquiring of the data set comprises: Sending a data request from a remote server via a network interface, wherein the data request carries a query condition; Receive a data set returned by the remote server based on the query condition.

4. The method according to claim 3, characterized in that The mapping table is a hash table or a dictionary.

5. The method according to claim 1, 2 or 4, characterized in that: The step of marking frequent sequences in each piece of business data includes: Frequent sequences are marked in business data through special tags, indexes, or pointers.

6. The method according to claim 5, characterized in that The selection mapping table includes: Select a mapping table from the preset mapping table set based on the business scenario.

7. The method according to claim 6, characterized in that Also includes: The support threshold is set and the mapping table is created based on the user's configuration operation.

8. A device for desensitizing business data, characterized in that: include: An acquisition unit, used to acquire a data set; the data set includes a plurality of business data of an enterprise; A marking unit, used to identify frequent sequences whose support is greater than a support threshold in the data set, and mark the frequent sequences in each piece of business data; A selection unit, used to select a mapping table; the mapping table is used to store a mapping relationship between an identifier and a frequent sequence; The conversion unit is used to replace the frequent sequence included in each business data with an identifier according to the mapping table to obtain a desensitized data set.

9. A computer storage medium, characterized in that The computer storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the method steps according to any one of claims 1 to 7.

10. A computer device, characterized in that: include: A processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the method steps as claimed in any one of claims 1 to 7.

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