A data desensitization method, device, equipment and storage medium

Abstract

The application discloses a data desensitization method and device, equipment and storage medium, and relates to the technical field of privacy protection, comprising: obtaining to-be-processed data from third-party business data based on a data access request sent by a third-party application, and performing authentication verification with the third-party application; if the verification is passed, determining a target desensitization strategy corresponding to the to-be-processed data according to a preset desensitization strategy set and preset user attribute information corresponding to the data access request, and judging whether the to-be-processed data is preset sensitive data according to a target sensitive word identification rule corresponding to the target desensitization strategy; if the to-be-processed data is not the preset sensitive data, directly sending the to-be-processed data to the third-party application; if the to-be-processed data is the preset sensitive data, performing a desensitization operation on the to-be-processed data based on a preset desensitization template corresponding to the target desensitization strategy, and sending the desensitized data to the third-party application. Thus, the application can maximize the reduction of sensitive data leakage in a business system.

Description

Technical Field

[0001] This invention relates to the field of privacy protection technology, and in particular to a data anonymization method, apparatus, device, and storage medium. Background Technology

[0002] Data masking is a key technology for protecting sensitive information. It mainly includes two forms: static masking and dynamic masking. Static masking is suitable for data masking in non-production environments, such as data sharing and distribution scenarios, where real-time business requirements are not necessary. Dynamic masking, on the other hand, is a real-time masking technology that performs masking during data usage, suitable for production environments such as production operations and maintenance, and business operations. In production processes, different personnel, environments, and access targets necessitate a flexible dynamic masking solution. Figure 1 As shown, current traditional data masking solutions are all fixed within business systems, such as using a default mask for the middle five digits of mobile phone numbers, which lacks flexibility. Therefore, how to achieve flexible data masking with multiple strategies and modes is an urgent problem to be solved. Summary of the Invention

[0003] In view of this, the purpose of this invention is to provide a data anonymization method, apparatus, device, and storage medium that is applicable to dynamic real-time anonymization in production environments, achieving flexible anonymization through multiple strategies and modes while minimizing the cost of modifying existing business systems. The specific solution is as follows:

[0004] Firstly, this application discloses a data anonymization method, including:

[0005] Data to be processed is obtained from third-party business data based on data access requests sent by third-party applications, and authentication and verification are performed with the third-party applications.

[0006] If the verification is successful, the target desensitization strategy corresponding to the data to be processed is determined according to the preset desensitization strategy set and the preset user attribute information corresponding to the data access request. The target sensitive word recognition rule corresponding to the target desensitization strategy is used to determine whether the data to be processed is preset sensitive data.

[0007] If the data is not the preset sensitive data, the data to be processed will be sent directly to the third-party application.

[0008] If it is the preset sensitive data, the data to be processed is desensitized based on the preset desensitization template corresponding to the target desensitization strategy, and the desensitized data is sent to the third-party application.

[0009] Optionally, before obtaining the data to be processed from the third-party business data based on the data access request sent by the third-party application, the method further includes:

[0010] The attributes of all users corresponding to the third-party application are divided to determine the user attribute information corresponding to each user; the attributes include the user's department and length of service.

[0011] The system manages a preset sensitive word library and associates each sensitive word in the library with a sensitive word recognition rule. The management includes adding sensitive words, updating sensitive words, and deleting any one or a combination of several of the sensitive words.

[0012] Set desensitization rules corresponding to each of the aforementioned sensitive words, and combine and bind the desensitization rules corresponding to the sensitive words and the sensitive word recognition rules to obtain the corresponding desensitization templates;

[0013] Based on the user attribute information, data source, access environment conditions, and the de-identification template, a corresponding de-identification strategy is determined; the access environment conditions include access time, access location, and the IP address of the third-party application.

[0014] Obtain access to the data source based on the pre-configured database address and / or file address.

[0015] Optionally, the step of obtaining the data to be processed from the third-party business data based on the data access request sent by the third-party application includes:

[0016] If the amount of data requested based on the data access request sent by the third-party application is less than the preset amount of data, then the data to be processed in the third-party business data is read and copied by the third-party backend service based on the data access request, and the data to be processed forwarded by the third-party backend service is obtained.

[0017] If the amount of data requested corresponding to the data access request sent by the third-party application is not less than the preset amount of data, then the data access request forwarded by the third-party backend service is obtained, and the data to be processed is obtained from the third-party business data based on the data access request.

[0018] Optionally, the method further includes:

[0019] If the amount of requested data corresponding to the data access request sent by the third-party application is not less than the preset data amount, then the corresponding identified data and the desensitized data corresponding to the identified data are cached based on the preset time period or the target data amount; wherein, the identified data is data that has undergone sensitive word identification but has not undergone the desensitization operation;

[0020] Determine whether the target desensitization strategy and target data ID corresponding to the data access request are the same as the cache desensitization strategy and identified data ID corresponding to the identified data;

[0021] If all are the same, directly read the desensitized data;

[0022] If the data IDs are the same but the desensitization strategies are different, then the desensitization operation is performed directly on the identified data;

[0023] If they are all different, the data to be processed is obtained from the third-party business data based on the data access request, and the sensitive word identification operation and the de-identification operation are performed on the data to be processed.

[0024] Optionally, the authentication verification with the third-party application includes:

[0025] Authentication is performed with the third-party application based on a temporary token or signature.

[0026] Optionally, after determining the target de-identification strategy corresponding to the data to be processed based on the preset de-identification strategy set and the preset user attribute information corresponding to the data access request, the method further includes:

[0027] If the target desensitization strategy does not have a target sensitive word recognition rule corresponding to the data to be processed, then the data to be processed will be subjected to sensitive word recognition operation based on the system's default sensitive word recognition rule.

[0028] Optionally, the step of performing desensitization on the data to be processed based on the preset desensitization template corresponding to the target desensitization strategy further includes:

[0029] If the data to be processed is desensitized using an attribute-based encryption method, then the access control policy for the data to be processed is encoded and encrypted.

[0030] Accordingly, after sending the anonymized data to the third-party application, the process also includes:

[0031] If the user attribute information corresponding to the third-party application conforms to the access control policy, the de-identified data is decrypted using a pre-set key corresponding to the user attribute information to obtain the decrypted data.

[0032] If the user attribute information corresponding to the third-party application does not conform to the access control policy, then the decryption operation of the anonymized data is prohibited.

[0033] Secondly, this application discloses a data desensitization device, comprising:

[0034] The pending data acquisition module is used to acquire pending data from third-party business data based on data access requests sent by third-party applications, and to perform authentication and verification with the third-party applications.

[0035] The target desensitization strategy determination module is used to determine the target desensitization strategy corresponding to the data to be processed based on the preset desensitization strategy set and the preset user attribute information corresponding to the data access request if the verification is passed.

[0036] The judgment module is used to determine whether the data to be processed is preset sensitive data according to the target sensitive word recognition rules corresponding to the target desensitization strategy;

[0037] The first data sending module is used to directly send the data to be processed to the third-party application if it is not the preset sensitive data;

[0038] The second data sending module is used to perform desensitization operation on the data to be processed based on the preset desensitization template corresponding to the target desensitization strategy if it is the preset sensitive data, and send the desensitized data to the third-party application.

[0039] Thirdly, this application discloses an electronic device, including:

[0040] Memory, used to store computer programs;

[0041] A processor is configured to execute the computer program to implement the steps of the aforementioned data desensitization method.

[0042] Fourthly, this application discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the steps of the aforementioned data desensitization method.

[0043] In this application, when performing data anonymization, the process first retrieves the data to be processed from the third-party business data based on a data access request sent by a third-party application, and performs authentication verification with the third-party application. If the verification passes, the target anonymization strategy corresponding to the data to be processed is determined according to a preset anonymization strategy set and preset user attribute information corresponding to the data access request. The target sensitive word identification rule corresponding to the target anonymization strategy is then used to determine whether the data to be processed is preset sensitive data. If it is not preset sensitive data, the data to be processed is directly sent to the third-party application. Finally, if it is preset sensitive data, the data to be processed is anonymized based on a preset anonymization template corresponding to the target anonymization strategy, and the anonymized data is sent to the third-party application. Therefore, this application determines the anonymization strategy corresponding to the data to be processed based on user attribute information, and identifies and anonymizes the data according to the anonymization strategy, achieving real-time dynamic anonymization based on attributes. This minimizes the leakage of sensitive data in existing business systems and reduces the cost of modifying existing business systems. This approach facilitates decoupling of business systems, making upgrades and maintenance easier. It is not limited to the development language of the business systems, allowing data interaction through a unified interface. Furthermore, different business systems can use the methods described in this application for data anonymization, greatly improving versatility. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0045] Figure 1 This is a schematic diagram of the desensitization design currently embedded in traditional business systems.

[0046] Figure 2 This is a flowchart of a data anonymization method disclosed in this application;

[0047] Figure 3 This is a schematic diagram of the functional architecture of a desensitization system disclosed in this application;

[0048] Figure 4 This is a schematic diagram of a specific data anonymization method disclosed in this application;

[0049] Figure 5 This is a schematic diagram of a specific data anonymization method disclosed in this application;

[0050] Figure 6 This is a schematic diagram of the structure of a data desensitization device disclosed in this application;

[0051] Figure 7 This is a structural diagram of an electronic device disclosed in this application. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] Current traditional data masking solutions are all fixed in business systems, such as using a default mask for the middle five digits of mobile phone numbers, which lacks flexibility. To solve the above-mentioned technical problems, this application discloses a data masking method that can achieve flexible masking with multiple strategies and modes.

[0054] See Figure 2 As shown, an embodiment of the present invention discloses a data anonymization method, comprising:

[0055] Step S11: Obtain the data to be processed from the third-party business data based on the data access request sent by the third-party application, and perform authentication verification with the third-party application.

[0056] In this embodiment, as Figure 3 As shown, this application provides a functional architecture for a data masking system. The dynamic data masking system mainly consists of three parts: an application terminal, a data masking system, and algorithm components. The application terminal mainly includes a data masking management client and a third-party application. The data masking management client is primarily used by administrators to manage sensitive data identification rules, data masking rules, and data masking tests. The third-party application calls the data masking system through an SDK (Software Development Kit) to perform data masking. The data masking system mainly provides services and interfaces for sensitive data identification, data masking, and related data masking management. For security reasons, the data masking system is usually deployed locally on the third-party business system. The algorithm components mainly include sensitive data identification algorithm components and data masking algorithm components. The user roles involved in the entire process mainly include system administrators and ordinary users. To facilitate user management, user roles can be consistent with those of the third-party application. System administrators are mainly responsible for formulating sensitive data rules, data masking rules, and data masking strategies based on business needs. Ordinary users are the main users accessing the data. By default, this system provides online dynamic data masking services to third-party applications, and users can view the data masking results through these applications. Users can also log in to the de-identification management client to view de-identification records.

[0057] This data anonymization system architecture, before retrieving data from third-party business data based on data access requests sent by third-party applications, categorizes the attributes of all users corresponding to the third-party application to determine the user attribute information for each user. These attributes include the user's department and length of service. Administrators generate or import basic user information from third-party applications and assign roles, permissions, and attributes to users (or applications), such as department and length of service, and generate public and private keys corresponding to these user attributes. These keys are primarily used in attribute-based encryption scenarios. The system also manages a preset sensitive word library and associates each sensitive word in the library with sensitive word recognition rules. This management includes adding, updating, and deleting any combination of these sensitive words. Furthermore, it allows for the classification of sensitive words by sensitivity level, such as high sensitivity, medium sensitivity, and low sensitivity.

[0058] The de-identification rules are managed by setting de-identification rules corresponding to each of the aforementioned sensitive words. These rules, along with the sensitive word identification rules, are then combined and bound to obtain corresponding de-identification templates. It should be noted that there is not a one-to-one correspondence between sensitive words and de-identification rules. De-identification rules can be set based on experience and knowledge. For example, in mobile phone numbers, the middle four or five digits can be masked. To facilitate business operations, a series of identification rules and de-identification rules are combined and bound to form a de-identification template. When third-party applications request data, they can carry a template ID (IdentityDocument, a type of number identifier), and the de-identification system performs data de-identification based on the template ID.

[0059] Managing data masking strategies: Administrators design corresponding data masking strategies based on different data resources, users, and access environments. For example, different users may use different masking methods or levels of masking for the same data. The corresponding masking strategy is determined based on the user attribute information, data source, access environment conditions, and the masking template; the access environment conditions include access time, access location, and the IP (Internet Protocol Address) address of the third-party application; the masking strategies are shown in Table 1.

[0060] Table 1

[0061]

[0062] Once the strategy is formulated, it is submitted to the dynamic de-identification system. It can then be updated or deleted as needed.

[0063] Data source management: Access permissions to the data source are obtained based on pre-configured database and / or file addresses. The system administrator pre-configures the database or file addresses in the data masking system to ensure the masking system has access to the data source. Because the data masking system accesses sensitive data, it is typically deployed locally. This way, as long as the data masking system is granted read access, sensitive data can be stored on a third-party server, not necessarily on the data masking system itself.

[0064] Furthermore, when retrieving data to be processed from third-party business data based on a data access request sent by a third-party application, if the requested data volume corresponding to the data access request sent by the third-party application is less than a preset data volume, then the data to be processed in the third-party business data is read and copied using a third-party backend service based on the data access request, and the data to be processed forwarded by the third-party backend service is obtained; if the requested data volume corresponding to the data access request sent by the third-party application is not less than the preset data volume, then the data access request forwarded by the third-party backend service is obtained, and the data to be processed is retrieved from the third-party business data based on the data access request. Upon obtaining the data to be processed, authentication verification is performed with the third-party application based on a temporary token or signature. This determines whether the data transmission process with the third-party application is secure.

[0065] Step S12: If the verification is successful, determine the target desensitization strategy corresponding to the data to be processed based on the preset desensitization strategy set and the preset user attribute information corresponding to the data access request, and determine whether the data to be processed is preset sensitive data based on the target sensitive word recognition rule corresponding to the target desensitization strategy.

[0066] In this embodiment, if the verification passes, data is read according to the request parameters, and the system obtains the sensitive word identification rules in the strategy for sensitive word identification. It then determines whether the data to be processed is preset sensitive data. If the target desensitization strategy does not have a target sensitive word identification rule corresponding to the data to be processed, then the system's default sensitive word identification rule is used to perform sensitive word identification on the data to be processed. That is, if the strategy is not bound to a sensitive word identification rule, the default sensitive word rule is used for sensitive word identification.

[0067] Step S13: If the data is not the preset sensitive data, the data to be processed is sent directly to the third-party application.

[0068] In this embodiment, after sensitive data identification is completed, non-sensitive data is directly returned to the third-party application without further desensitization.

[0069] Step S14: If it is the preset sensitive data, then the data to be processed is desensitized based on the preset desensitization template corresponding to the target desensitization strategy, and the desensitized data is sent to the third-party application.

[0070] In this embodiment, for identified sensitive data, the desensitization system verifies the user's attributes and corresponding desensitization strategies. After successful verification, the data is desensitized according to the desensitization strategy, and the result is returned to the third-party application. This involves using desensitization rules within the desensitization rules for corresponding desensitization. Typically, desensitization rules employ masking, partial replacement, and generalization methods, preserving some characteristics or data of the original data. Alternatively, k-anonymity and differential privacy methods can be used. In this case, the administrator needs to pre-set a privacy budget, such as the k value in k-anonymity and the ε value in differential privacy. When an access request exceeds the privacy budget, the user's access will be terminated. Furthermore, the desensitization method can be irreversible algorithms such as masking, replacement, and hashing, or reversible encryption such as AES (Advanced Encryption Standard) or ABE (Attribute-Based Encryption). If the desensitization operation is performed on the data to be processed using attribute-based encryption, the access control policy for the data to be processed is encoded and encrypted. If the user attribute information corresponding to the third-party application conforms to the access control policy, the de-identified data is decrypted using a pre-set key corresponding to the user attribute information to obtain the decrypted data. If the user attribute information corresponding to the third-party application does not conform to the access control policy, decryption of the de-identified data is prohibited. In other words, if an attribute-based approach (such as the ABE scheme) is used, the de-identification module needs to encode and encrypt the data's access control policy (such as an LSSS (Linear Secret-Sharing Scheme) matrix approach). Optionally, for relatively fixed access control policies, the data can be encrypted according to the predetermined access policy before access. It should be noted that for users whose attributes conform to the policy, decryption is required through the client to view the actual data. For users whose attributes do not conform to the policy, the data cannot be decrypted using their private key; the user can only see the encrypted information.

[0071] As can be seen from the above, when performing data anonymization, this application first obtains the data to be processed from the third-party business data based on the data access request sent by the third-party application, and performs authentication verification with the third-party application. If the verification is successful, the target anonymization strategy corresponding to the data to be processed is determined according to the preset anonymization strategy set and the preset user attribute information corresponding to the data access request. The target sensitive word identification rule corresponding to the target anonymization strategy is then used to determine whether the data to be processed is preset sensitive data. If it is not preset sensitive data, the data to be processed is directly sent to the third-party application. Finally, if it is preset sensitive data, the data to be processed is anonymized based on the preset anonymization template corresponding to the target anonymization strategy, and the anonymized data is sent to the third-party application. Therefore, this application determines the anonymization strategy corresponding to the data to be processed based on user attribute information, and identifies and anonymizes the data according to the anonymization strategy, achieving real-time dynamic anonymization based on attributes. This minimizes the leakage of sensitive data in existing business systems and reduces the cost of modifying existing business systems. This approach facilitates decoupling of business systems, making upgrades and maintenance easier. It is not limited to the development language of the business systems, allowing data interaction through a unified interface. Furthermore, different business systems can use the methods described in this application for data anonymization, greatly improving versatility.

[0072] As can be seen from the above embodiments, this application can achieve dynamic data anonymization based on attributes, and it is applicable to both scenarios with small application request data and big data scenarios. Next, the data anonymization process for scenarios with small application request data will be described in detail. See [link to documentation]. Figure 4 As shown in the figure, this application discloses a specific data anonymization method, including:

[0073] The specific process in this embodiment is mainly aimed at scenarios where the application requests a small amount of data, such as less than 10M (MByte, a storage unit).

[0074] First, the third-party backend service receives a data access request from a third-party application or client. After successful verification, the third-party backend service queries the business database. Once the database connection is successful, the data is read into memory. In addition to reading business data, the service also reads relevant user attribute information. The third-party backend service sends the data to be processed and related attributes to the dynamic desensitization system. Initially, the data to be processed is considered non-sensitive, so the third party does not need to call the desensitization system interface and can directly access the corresponding data. Upon receiving the access request, the desensitization system and the third-party application perform authentication, such as using a token or signature. After successful verification, the system reads the data according to the request parameters and uses the sensitive word identification rules in the policy to identify sensitive words. If the policy does not have a bound sensitive word identification rule, the default sensitive word rule is used for sensitive word identification.

[0075] After sensitive data identification is completed, non-sensitive data is directly returned to the third-party application. For sensitive data, the de-identification system queries the user's corresponding de-identification policy and performs dynamic de-identification operations according to the de-identification template in the policy. The de-identified result is then returned to the third-party backend service, which in turn returns the de-identified data to the client.

[0076] As can be seen, this application determines the desensitization strategy corresponding to the data to be processed based on user attribute information, and identifies and desensitizes the data according to the desensitization strategy, realizing real-time dynamic desensitization based on attributes, minimizing the leakage of sensitive data in the existing business system, and reducing the transformation cost of the existing business system.

[0077] The following section will describe in detail the data anonymization process for big data scenarios. (See also...) Figure 5 As shown in the figure, this application discloses a specific data anonymization method, including:

[0078] As can be seen from the above embodiments, when the data volume is small, the third-party backend service receives a data access request from a third-party application or client; after successful verification, the third-party backend service queries the business database; after a successful database connection, the data is read into memory; in addition to reading business data, the user's relevant attribute information is also read; the third-party backend service sends the data to be processed and related attributes to the dynamic de-identification system. In this embodiment, in a big data scenario, the dynamic de-identification system directly connects to the third-party application's database or file system. No additional data copying is required. That is, after receiving a data request, the third-party backend service directly sends the data request to the dynamic de-identification system, which interacts with the third-party business data and directly obtains the data to be processed from the third-party business data.

[0079] The system reads data based on the request parameters and retrieves the sensitive word identification rules from the policy to identify sensitive words. If the policy does not have a specific sensitive word identification rule, the default rule is used. After sensitive word identification, non-sensitive data is directly returned to the third-party application. For sensitive data, the caching system is used for caching. Simultaneously, for sensitive data, the de-identification system queries the user's corresponding de-identification policy and performs dynamic de-identification based on the de-identification template in the policy. The de-identified results are then synchronously cached in the caching system and returned to the third-party backend service.

[0080] It should be noted that when the data volume is small, the de-identified data is not cached by default. For complex de-identification algorithms or sensitive word recognition algorithms, and when the number of users accessing the system is large, the de-identification system will perform repeated data processing operations. To improve access real-time performance, in big data scenarios, the de-identification system adopts a caching service. The specific access strategy is not constrained by this invention. Based on a preset time period or target data volume, the corresponding identified data and the de-identified data corresponding to the identified data are cached. The identified data refers to data that has undergone sensitive word recognition but has not undergone the de-identification operation. It is determined whether the target de-identification strategy and target data ID corresponding to the data access request are the same as the cache de-identification strategy and identified data ID corresponding to the identified data. If they are the same, the de-identified data is read directly. If the data IDs are the same but the de-identification strategies are different, the de-identification operation is performed directly on the identified data. If they are different, the data to be processed is obtained from the third-party business data based on the data access request, and the sensitive word recognition operation and the de-identification operation are performed on the data to be processed. In other words, when a third-party application accesses a piece of data, the data masking system checks if there is any masked data in the current cache system that has the same access masking policy ID and the same data ID. If so, it directly reads the masking result. If the data ID is the same but the masking policy is different, it directly reads the cached data for masking. If neither matches, the data masking system reads the data from the database and performs sensitivity identification and masking operations.

[0081] As can be seen, this application is suitable for large-scale data scenarios, reducing data copying and improving processing efficiency. At the same time, the caching mechanism reduces the need for sensitive data identification and de-identification operations.

[0082] See Figure 6 As shown, an embodiment of the present invention discloses a data desensitization device, comprising:

[0083] The pending data acquisition module 11 is used to acquire pending data from third-party business data based on data access requests sent by third-party applications, and to perform authentication and verification with the third-party applications.

[0084] The target desensitization strategy determination module 12 is used to determine the target desensitization strategy corresponding to the data to be processed based on the preset desensitization strategy set and the preset user attribute information corresponding to the data access request if the verification is passed.

[0085] The judgment module 13 is used to determine whether the data to be processed is preset sensitive data according to the target sensitive word recognition rule corresponding to the target desensitization strategy;

[0086] The first data sending module 14 is used to directly send the data to be processed to the third-party application if it is not the preset sensitive data;

[0087] The second data sending module 15 is used to perform desensitization operation on the data to be processed based on the preset desensitization template corresponding to the target desensitization strategy if it is the preset sensitive data, and send the desensitized data to the third-party application.

[0088] As can be seen from the above, when performing data anonymization, this application first obtains the data to be processed from the third-party business data based on the data access request sent by the third-party application, and performs authentication verification with the third-party application. If the verification is successful, the target anonymization strategy corresponding to the data to be processed is determined according to the preset anonymization strategy set and the preset user attribute information corresponding to the data access request. The target sensitive word identification rule corresponding to the target anonymization strategy is then used to determine whether the data to be processed is preset sensitive data. If it is not preset sensitive data, the data to be processed is directly sent to the third-party application. Finally, if it is preset sensitive data, the data to be processed is anonymized based on the preset anonymization template corresponding to the target anonymization strategy, and the anonymized data is sent to the third-party application. Therefore, this application determines the anonymization strategy corresponding to the data to be processed based on user attribute information, and identifies and anonymizes the data according to the anonymization strategy, achieving real-time dynamic anonymization based on attributes. This minimizes the leakage of sensitive data in existing business systems and reduces the cost of modifying existing business systems. This approach facilitates decoupling of business systems, making upgrades and maintenance easier. It is not limited to the development language of the business systems, allowing data interaction through a unified interface. Furthermore, different business systems can use the methods described in this application for data anonymization, greatly improving versatility.

[0089] In some specific embodiments, the device can also be used to classify the attributes of all users corresponding to the third-party application to determine the user attribute information corresponding to each user; the attributes include the user's department and length of service; manage a preset sensitive word library and associate each sensitive word in the preset sensitive word library with a sensitive word recognition rule; the management includes adding the sensitive word, updating the sensitive word, and deleting any one or a combination of the sensitive words; set de-identification rules corresponding to each sensitive word, and combine and bind the de-identification rules corresponding to the sensitive words with the sensitive word recognition rules to obtain a corresponding de-identification template; determine a corresponding de-identification strategy based on the user attribute information, data source, access environment conditions, and the de-identification template; the access environment conditions include access time, access location, and the IP address of the third-party application; and obtain access permissions to the data source according to a pre-configured database address and / or file address.

[0090] In some specific embodiments, the data to be processed acquisition module 11 can be specifically used to: if the amount of request data corresponding to the data access request sent by the third-party application is less than a preset amount of data, then read and copy the data to be processed from the third-party business data based on the data access request using a third-party backend service, and obtain the data to be processed forwarded by the third-party backend service; if the amount of request data corresponding to the data access request sent by the third-party application is not less than the preset amount of data, then obtain the data access request forwarded by the third-party backend service, and obtain the data to be processed from the third-party business data based on the data access request.

[0091] In some specific embodiments, the device can also be used to cache the corresponding identified data and the desensitized data corresponding to the identified data based on a preset time period or target data volume if the requested data volume corresponding to the data access request sent based on the third-party application is not less than the preset data volume; wherein, the identified data is data that has undergone sensitive word identification but has not undergone the desensitization operation; determine whether the target desensitization strategy and target data ID corresponding to the data access request are the same as the cache desensitization strategy and identified data ID corresponding to the identified data; if they are the same, directly read the desensitized data; if the data IDs are the same but the desensitization strategies are different, directly perform the desensitization operation on the identified data; if they are not the same, obtain the data to be processed from the third-party business data based on the data access request, and perform the sensitive word identification operation and the desensitization operation on the data to be processed.

[0092] In some specific embodiments, the data acquisition module 11 can be used to authenticate and verify the third-party application based on a temporary token or signature.

[0093] In some specific embodiments, the device can also be used to perform sensitive word recognition operation on the data to be processed based on the system's default sensitive word recognition rule if there is no target sensitive word recognition rule corresponding to the data to be processed in the target desensitization strategy.

[0094] In some specific embodiments, the device can also be used to encode and encrypt the access control policy of the data to be processed if the data to be processed is de-identified using an attribute-based encryption method; if the user attribute information corresponding to the third-party application conforms to the access control policy, the de-identified data is decrypted using a pre-set key corresponding to the user attribute information to obtain decrypted data; if the user attribute information corresponding to the third-party application does not conform to the access control policy, the decryption operation on the de-identified data is prohibited.

[0095] Furthermore, embodiments of this application also disclose an electronic device, Figure 7 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0096] Figure 7 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the data desensitization method disclosed in any of the foregoing embodiments. Furthermore, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0097] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0098] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0099] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the data desensitization method executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include a computer program capable of performing other specific tasks.

[0100] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned data desensitization method. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0101] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0102] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0103] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0104] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0105] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A data de-sensitization method, characterized in that, include: Data to be processed is obtained from third-party business data based on data access requests sent by third-party applications, and authentication and verification are performed with the third-party applications. If the verification is successful, the target desensitization strategy corresponding to the data to be processed is determined according to the preset desensitization strategy set and the preset user attribute information corresponding to the data access request. The target sensitive word recognition rule corresponding to the target desensitization strategy is used to determine whether the data to be processed is preset sensitive data. If the data is not the preset sensitive data, the data to be processed will be sent directly to the third-party application. If it is the preset sensitive data, then the data to be processed is desensitized based on the preset desensitization template corresponding to the target desensitization strategy, and the desensitized data is sent to the third-party application; Before the data access request sent by the third-party application retrieves the data to be processed from the third-party business data, the process also includes: The operation involves managing a preset sensitive word library and associating each sensitive word in the preset sensitive word library with sensitive word recognition rules; Set desensitization rules corresponding to each of the aforementioned sensitive words, and combine and bind the desensitization rules corresponding to the sensitive words and the sensitive word recognition rules to obtain the corresponding desensitization templates; The corresponding de-identification strategy is determined based on user attribute information, data source, access environment conditions, and the de-identification template. The data access request sent by the third-party application retrieves the data to be processed from the third-party business data, including: If the amount of data requested based on the data access request sent by the third-party application is less than the preset amount of data, then the data to be processed in the third-party business data is read and copied by the third-party backend service based on the data access request, and the data to be processed forwarded by the third-party backend service is obtained. If the amount of data requested corresponding to the data access request sent by the third-party application is not less than the preset amount of data, then the data access request forwarded by the third-party backend service is obtained, and the data to be processed is obtained from the third-party business data based on the data access request. The method further includes: If the amount of requested data corresponding to the data access request sent by the third-party application is not less than the preset data amount, then the corresponding identified data and the desensitized data corresponding to the identified data are cached based on the preset time period or the target data amount; wherein, the identified data is data that has undergone sensitive word identification but has not undergone the desensitization operation; Determine whether the target desensitization strategy and target data ID corresponding to the data access request are the same as the cache desensitization strategy and identified data ID corresponding to the identified data; If all are the same, directly read the desensitized data; If the data IDs are the same but the desensitization strategies are different, then the desensitization operation is performed directly on the identified data; If they are all different, the data to be processed is obtained from the third-party business data based on the data access request, and the sensitive word identification operation and the de-identification operation are performed on the data to be processed.

2. The data anonymization method according to claim 1, characterized in that, Before the data access request sent by the third-party application retrieves the data to be processed from the third-party business data, the process also includes: The attributes of all users corresponding to the third-party application are divided to determine the user attribute information corresponding to each user; the attributes include the user's department and length of service. The management includes adding the sensitive words, updating the sensitive words, and deleting any one or a combination of several of the sensitive words; The access environment conditions include access time, access location, and the IP address of the third-party application; Obtain access to the data source based on the pre-configured database address and / or file address.

3. The data anonymization method according to claim 1, characterized in that, The authentication and verification with the third-party application includes: Authentication is performed with the third-party application based on a temporary token or signature.

4. The data anonymization method according to claim 1, characterized in that, After determining the target de-identification strategy corresponding to the data to be processed based on the preset de-identification strategy set and the preset user attribute information corresponding to the data access request, the method further includes: If the target desensitization strategy does not have a target sensitive word recognition rule corresponding to the data to be processed, then the data to be processed will be subjected to sensitive word recognition operation based on the system's default sensitive word recognition rule.

5. The data desensitization method according to any one of claims 1 to 4, characterized in that, The step of performing desensitization on the data to be processed based on the preset desensitization template corresponding to the target desensitization strategy further includes: If the data to be processed is desensitized using an attribute-based encryption method, then the access control policy for the data to be processed is encoded and encrypted. Accordingly, after sending the anonymized data to the third-party application, the process also includes: If the user attribute information corresponding to the third-party application conforms to the access control policy, the de-identified data is decrypted using a pre-set key corresponding to the user attribute information to obtain the decrypted data. If the user attribute information corresponding to the third-party application does not conform to the access control policy, then the decryption operation of the anonymized data is prohibited.

6. A data anonymization device, characterized in that, include: The pending data acquisition module is used to acquire pending data from third-party business data based on data access requests sent by third-party applications, and to perform authentication and verification with the third-party applications. The target desensitization strategy determination module is used to determine the target desensitization strategy corresponding to the data to be processed based on the preset desensitization strategy set and the preset user attribute information corresponding to the data access request if the verification is passed. The judgment module is used to determine whether the data to be processed is preset sensitive data according to the target sensitive word recognition rules corresponding to the target desensitization strategy; The first data sending module is used to directly send the data to be processed to the third-party application if it is not the preset sensitive data; The second data sending module is used to perform desensitization operation on the data to be processed based on the preset desensitization template corresponding to the target desensitization strategy if it is the preset sensitive data, and send the desensitized data to the third-party application; The device is also used to manage a preset sensitive word library and associate each sensitive word in the preset sensitive word library with a sensitive word recognition rule; Define desensitization rules corresponding to each of the aforementioned sensitive words, and combine and bind the desensitization rules corresponding to the sensitive words with the sensitive word recognition rules to obtain corresponding desensitization templates; determine corresponding desensitization strategies based on user attribute information, data sources, access environment conditions, and the desensitization templates; The pending data acquisition module is used to read and copy the pending data from the third-party business data based on the data access request sent by the third-party application if the amount of request data corresponding to the data access request sent by the third-party application is less than the preset amount of data, and to obtain the pending data forwarded by the third-party backend service. If the amount of data requested corresponding to the data access request sent by the third-party application is not less than the preset amount of data, then the data access request forwarded by the third-party backend service is obtained, and the data to be processed is obtained from the third-party business data based on the data access request. The device is further configured to, if the amount of requested data corresponding to the data access request sent by the third-party application is not less than the preset data amount, cache the corresponding identified data and the desensitized data corresponding to the identified data based on a preset time period or a target data amount; wherein, the identified data is data that has undergone sensitive word identification but has not undergone the desensitization operation; determine whether the target desensitization strategy and target data ID corresponding to the data access request are the same as the cache desensitization strategy and identified data ID corresponding to the identified data; if they are the same, directly read the desensitized data; if the data IDs are the same but the desensitization strategies are different, directly perform the desensitization operation on the identified data; If they are all different, the data to be processed is obtained from the third-party business data based on the data access request, and the sensitive word identification operation and the de-identification operation are performed on the data to be processed.

7. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the steps of the data desensitization method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, Used for storing computer programs; wherein, when the computer program is executed by a processor, it implements the steps of the data desensitization method as described in any one of claims 1 to 5.

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