Data desensitization processing method and device

Abstract

The invention provides a data desensitization processing method and device, and the method comprises the steps: carrying out the desensitization processing of target data, and obtaining the desensitization data corresponding to the target data; the desensitization data comprises a plurality of versions, and desensitization processing modes adopted by the desensitization data of each version are different; performing data feature extraction on the target data to obtain original feature information corresponding to the target data; storing the desensitized data corresponding to the target data and the original feature information in a first storage area; for the target data, selecting one version of desensitized data from the desensitized data corresponding to the target data in the first storage area, and storing the selected desensitized data and the original feature information as service available data of the target data in a second storage area; the second storage area can be accessed by a service application to realize desensitization use of the target data in a service scene. The device is used for executing the method. According to the data desensitization processing method and device provided by the embodiment of the invention, the use safety of the data is improved.

Description

Technical Field

[0001] This invention relates to the field of big data technology, specifically to a data anonymization processing method and apparatus. Background Technology

[0002] With the widespread application of new technologies such as big data, artificial intelligence, and cloud computing in the financial industry, data has gradually transformed from an information asset into a production factor, and its importance is becoming increasingly prominent. To facilitate data use for internal data statistics, analysis, and risk modeling while ensuring data security, data anonymization is necessary.

[0003] In existing technologies, data masking methods are divided into static data masking and dynamic data masking. Static masking involves extracting the cleaned and integrated raw data, processing it according to data masking strategies and rules, and storing it in the target database. Dynamic masking involves users directly accessing the raw data, and masking the data involved during the access process. In big data scenarios, it is crucial to ensure both internal and external data security, preventing security risks from external data applications and internal data leakage risks. Therefore, proposing a data masking method that can improve data security has become an important issue that urgently needs to be addressed in this field. Summary of the Invention

[0004] To address the problems in the prior art, embodiments of the present invention provide a data desensitization processing method and apparatus, which can at least partially solve the problems existing in the prior art.

[0005] In a first aspect, the present invention proposes a data anonymization processing method, comprising:

[0006] The target data is anonymized to obtain anonymized data corresponding to the target data; wherein, the anonymized data includes multiple versions, and each version of the anonymized data uses a different anonymization processing method;

[0007] Data feature extraction is performed on the target data to obtain the original feature information corresponding to the target data;

[0008] The de-identified data corresponding to the target data and the original feature information corresponding to the target data are stored in the first storage area; the first storage area cannot be accessed by business applications.

[0009] For the target data, select one version of the de-identified data from the de-identified data corresponding to the target data in the first storage area, and store the selected de-identified data and the original feature information corresponding to the target data as the business-usable data of the target data in the second storage area; the second storage area can be accessed by business applications to realize the de-identified use of the target data in business scenarios.

[0010] In a second aspect, the present invention provides a data desensitization processing apparatus, comprising:

[0011] The desensitization processing unit is used to desensitize the target data to obtain desensitized data corresponding to the target data; wherein, the desensitized data includes multiple versions, and each version of the desensitized data adopts a different desensitization processing method;

[0012] The feature extraction unit is used to extract data features from the target data to obtain the original feature information corresponding to the target data.

[0013] The first storage processing unit is used to store the de-identified data corresponding to the target data and the original feature information corresponding to the target data into a first storage area; the first storage area cannot be accessed by business applications.

[0014] The second storage processing unit is used to select a version of de-identified data from the de-identified data corresponding to the target data in the first storage area for the target data, and store the selected de-identified data and the original feature information corresponding to the target data as the business-usable data of the target data in the second storage area; the second storage area can be accessed by business applications to realize the de-identified use of the target data in business scenarios.

[0015] Thirdly, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the data desensitization processing method described in any of the above embodiments.

[0016] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the data desensitization processing method described in any of the above embodiments.

[0017] Fifthly, the present invention provides a computer program product, the computer program product comprising a computer program, which, when executed by a processor, implements the data desensitization processing method described in any of the above embodiments.

[0018] The data anonymization processing method and apparatus provided in this invention can anonymize target data to obtain anonymized data corresponding to the target data. The anonymized data includes multiple versions, each employing a different anonymization processing method. Data features are extracted from the target data to obtain the original feature information corresponding to the target data. The anonymized data and the original feature information corresponding to the target data are stored in a first storage area, which is inaccessible to business applications. For the target data, one version of the anonymized data is selected from the anonymized data in the first storage area, and the selected anonymized data and the original feature information corresponding to the target data are stored as business-usable data of the target data in a second storage area. The second storage area is accessible to business applications to enable anonymized use of the target data in business scenarios. Because multiple versions of anonymized data are generated and the anonymization methods of each version differ, different versions of anonymized data can be provided for external use. Furthermore, the business-usable data of the target data is stored separately from the anonymized data corresponding to the target data, improving data security. Attached Figure Description

[0019] 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 some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0020] Figure 1 This is a schematic flowchart of the data desensitization processing method provided in the first embodiment of the present invention.

[0021] Figure 2 This is a schematic flowchart of the data desensitization processing method provided in the second embodiment of the present invention.

[0022] Figure 3 This is a schematic flowchart of the data desensitization processing method provided in the third embodiment of the present invention.

[0023] Figure 4 This is a schematic diagram of a version of de-identified data provided in the fourth embodiment of the present invention.

[0024] Figure 5 This is a schematic diagram of the data desensitization processing device provided in the fifth embodiment of the present invention.

[0025] Figure 6 This is a schematic diagram of the data desensitization processing device provided in the sixth embodiment of the present invention.

[0026] Figure 7 This is a schematic diagram of the data desensitization processing device provided in the seventh embodiment of the present invention.

[0027] Figure 8 This is a schematic diagram of the data desensitization processing device provided in the eighth embodiment of the present invention.

[0028] Figure 9 This is a schematic diagram of the data desensitization processing device provided in the ninth embodiment of the present invention.

[0029] Figure 10 This is a schematic diagram of the data desensitization processing device provided in the tenth embodiment of the present invention.

[0030] Figure 11 This is a schematic diagram of the physical structure of the electronic device provided in the eleventh embodiment of the present invention. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments and descriptions of the present invention are used to explain the present invention, but are not intended to limit the present invention. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other.

[0032] The acquisition, storage, use, and processing of data in the technical solution of this application all comply with relevant laws and regulations. The user information in the embodiments of this application is obtained through legal and compliant means, and the acquisition, storage, use, and processing of user information are all authorized and agreed upon by the client.

[0033] To facilitate understanding of the technical solution provided in this application, the relevant content of the technical solution in this application will be explained below.

[0034] Sensitive Information: Sensitive information refers to information that may have a significant impact on the interests or reputation of individuals, organizations, or businesses. In this document, sensitive information includes personal and merchant information in online payment transactions, including but not limited to names, ID numbers, mobile phone numbers, bank account numbers, and home addresses.

[0035] Data encryption and decryption: Encryption is the process of transforming plaintext into ciphertext using mathematical methods and a key pair, completely hiding its true content; decryption is the reverse process, restoring the plaintext to its original form using mathematical methods. Data encryption is used to ensure the confidentiality of information transmission. Encryption methods are mainly divided into symmetric encryption, asymmetric encryption, and irreversible encryption. Symmetric encryption uses the same key for both encryption and decryption. Asymmetric encryption uses a pair of keys, a public key and a private key, which are mathematically related and cannot be derived from one. Encryption is done using the public key, and decryption is done using the private key. Irreversible encryption means that once processed by a mathematical encryption algorithm, the original ciphertext cannot be retrieved.

[0036] Data anonymization: Data anonymization refers to the transformation of certain sensitive information to reliably protect sensitive and private data. Data anonymization should adhere to two principles as much as possible: First, retain as much meaningful information as possible from the original data after anonymization. Second, minimize the risk of hacking and ensure data security.

[0037] Data Acquisition: Data acquisition refers to using a device to collect data from outside the system and store it in the current system. Data acquisition can gather data from various business systems into a big data platform. Data acquisition is divided into offline acquisition and real-time acquisition. Offline acquisition can collect transaction data from day T into the big data platform in batches on day T+1. Real-time acquisition, through a streaming processing engine, can enable data to enter the big data platform within seconds or even milliseconds after entering the transaction system.

[0038] In the context of big data, users typically access billions or even tens of billions of data details, making dynamic data masking inefficient for daily data usage and analysis. Currently, most big data platforms employ static data masking for sensitive data. By using configuration templates, a certain degree of masking flexibility is ensured, and by introducing factors such as text features, time-domain features, frequency-domain features, and convolutional features, some original data characteristics are preserved.

[0039] In big data scenarios, it is crucial to ensure the security of both internal and external data usage. This involves preventing security risks from external data applications as well as internal data leakage risks. This application proposes generating multiple versions of de-identified data for sensitive data, with each version employing different de-identification methods. This facilitates providing different versions of de-identified data for external use, thereby improving data security.

[0040] There are usually limitations to the de-identified data in terms of reflecting the original data characteristics. For example, if the data is too long, it may not fully represent the original data characteristics and may not be easily expanded. Adding too much feature information will make the de-identified data too long, which is not conducive to subsequent storage and analysis. Therefore, de-identified data should retain a sufficient number of original data characteristics. In this application, the de-identification process and data feature extraction are handled separately. By extracting data features from the original data separately, a sufficient number of original data characteristics can be retained in the original feature information.

[0041] Current big data anonymization rules are relatively simplistic, posing security risks under conditions of high data security requirements. This application proposes that data anonymization methods and the anonymized data used externally be updated regularly to further improve data security. Furthermore, in the event of a data breach, the anonymization rules should be changed promptly, and the anonymized data used externally should be updated.

[0042] The following description uses a server as an example to illustrate the specific implementation process of the data anonymization method provided in this embodiment of the invention. It should be understood that the execution entity of the data anonymization method provided in this embodiment of the invention is not limited to a server.

[0043] Figure 1 This is a flowchart illustrating the data anonymization processing method provided in the first embodiment of the present invention, as shown below. Figure 1 As shown, the data anonymization processing method provided in this embodiment of the invention includes:

[0044] S101. De-identify the target data to obtain de-identified data corresponding to the target data; wherein, the de-identified data includes multiple versions, and each version of the de-identified data adopts a different de-identification processing method;

[0045] Specifically, the server acquires the target data, performs de-identification processing on the target data, and obtains the corresponding de-identified data. The de-identified data corresponding to the target data includes multiple versions, each employing a different de-identification processing method. The target data contains sensitive information and requires de-identification processing to protect this sensitive data. The de-identification processing method is set according to actual needs, and this embodiment of the invention does not impose limitations. In this embodiment of the invention, a version of the de-identified data can also be referred to as a de-identification tag information.

[0046] Generating multiple versions of de-identified data at once is to allow for the selection of a version of de-identified data that has not been used externally when updating the business-available data of the target data stored in the second storage area, thereby improving data update efficiency and saving time.

[0047] For example, bank customer data including names, ID card numbers, mobile phone numbers, and bank account numbers are all sensitive data. The server can obtain the above bank customer data and perform de-identification processing to obtain the de-identified data corresponding to the bank customer data.

[0048] For example, data anonymization can include two processes: data anonymization and data encryption. Different anonymization methods may involve different data anonymization rules, different encryption algorithms, or even different data anonymization rules and encryption algorithms.

[0049] S102. Extract data features from the target data to obtain the original feature information corresponding to the target data;

[0050] Specifically, the server performs feature extraction on the target data to obtain the original feature information corresponding to the target data. This original feature information includes, but is not limited to, text features, time-domain features, frequency-domain features, and convolutional features, and can be set according to actual needs; this embodiment of the invention does not impose any limitations.

[0051] S103. Store the de-identified data corresponding to the target data and the original feature information corresponding to the target data in a first storage area; the first storage area cannot be accessed by business applications.

[0052] Specifically, after obtaining the anonymized data and the original feature information corresponding to the target data, the server can store these two data points in a first storage area. The data stored in the first storage area is not publicly accessible and cannot be accessed by business applications.

[0053] The de-identified data corresponding to the target data and the original feature information corresponding to the target data are independent of each other. While ensuring data security, the scalability of the original feature information can be improved.

[0054] For example, the raw data containing sensitive information collected in the first storage area cannot be accessed by business applications. Temporary data generated from de-identifying and extracting data features from the target data, as well as the de-identified data and original feature information corresponding to the target data obtained at the end, are also stored in the first storage area and cannot be accessed by business applications.

[0055] S104. For the target data, select a version of the de-identified data from the de-identified data corresponding to the target data in the first storage area, and store the selected de-identified data and the original feature information corresponding to the target data as the business-usable data of the target data in the second storage area; the second storage area can be accessed by business applications to realize the de-identified use of the target data in business scenarios.

[0056] Specifically, for the target data, the server selects a version of de-identified data from the de-identified data corresponding to the target data in the first storage area, and uses the selected de-identified data and the original feature information corresponding to the target data as the business-usable data of the target data. The business-usable data of the target data is then stored in the second storage area. The data stored in the second storage area is publicly accessible and can be accessed by business applications to enable the de-identified use of the target data in business scenarios. The first storage area and the second storage area are isolated.

[0057] For example, the original feature information corresponding to the target data can be appended to the selected de-identified data to obtain the business-usable data of the target data. Furthermore, the business-usable data of the target data may also include other business data, which are business data that do not require data de-identification and feature extraction.

[0058] By storing the business-usable data of the target data in a second storage area and storing it in isolation from the de-identified data corresponding to the target data, the security of data use can be improved.

[0059] The data anonymization processing method provided in this invention can anonymize target data to obtain anonymized data corresponding to the target data. The anonymized data includes multiple versions, each employing a different anonymization processing method. Data features are extracted from the target data to obtain the original feature information corresponding to the target data. The anonymized data and the original feature information corresponding to the target data are stored in a first storage area, which is inaccessible to business applications. For the target data, one version of the anonymized data is selected from the anonymized data in the first storage area, and the selected anonymized data and the original feature information corresponding to the target data are stored as business-usable data of the target data in a second storage area. The second storage area is accessible to business applications to enable the anonymized use of the target data in business scenarios. Because multiple versions of anonymized data are generated and the anonymization methods of each version differ, different versions of anonymized data can be provided for external use. Furthermore, the business-usable data of the target data is stored separately from the anonymized data corresponding to the target data, improving data security.

[0060] Based on the above embodiments, the data desensitization processing method provided by the embodiments of the present invention further includes:

[0061] Based on the de-identified data corresponding to the target data stored in the first storage area, update the de-identified data in the service-available data of the target data in the second storage area.

[0062] Specifically, to further enhance data security, the server can select a version of the de-identified data that has not been used externally from the de-identified data corresponding to the target data in the first storage area, and update the de-identified data in the business-available data of the target data in the second storage area. It is understood that the external usage status of each version of the de-identified data corresponding to the target data can be recorded. If a version of the de-identified data corresponding to the target data has been selected to constitute the business-available data of the target data, then the external usage status is "used externally." If a version of the de-identified data corresponding to the target data has not been selected to constitute the business-available data of the target data, then the external usage status is "not used externally."

[0063] Figure 2 This is a flowchart illustrating the data desensitization processing method provided in the second embodiment of the present invention, as shown below. Figure 2 As shown, based on the above embodiments, further, the process of de-identifying the target data to obtain the de-identified data corresponding to the target data includes:

[0064] S201. Based on the sensitive data type of the target data, obtain the de-identification template corresponding to the target data; wherein, the correspondence between the sensitive data type and the de-identification template is preset;

[0065] Specifically, the target data has a sensitive data type, and the corresponding de-identification template can be obtained by querying based on the sensitive data type of the target data. The correspondence between the sensitive data type and the de-identification template is preset; the de-identification version is preset, including data de-identification rules and encryption algorithms corresponding to each preset version, which are set according to actual needs, and this embodiment of the invention does not impose limitations.

[0066] S202. Decrypt the target data to obtain the original plaintext data corresponding to the target data;

[0067] Specifically, in order to prevent data leakage, the target data is usually stored in encryption. In order to perform desensitization processing, the target data needs to be decrypted to obtain the original plaintext data corresponding to the target data.

[0068] S203. According to the data anonymization rules corresponding to each preset version, the original plaintext data is anonymized to generate multiple intermediate data corresponding to the target data; wherein the data anonymization rules corresponding to each version are different.

[0069] Specifically, the server performs data anonymization processing on the original plaintext data corresponding to the target data according to preset data anonymization rules for each version, generating multiple intermediate data corresponding to the target data. Each intermediate data corresponds to a data anonymization rule for a version, and the corresponding data anonymization rules for each version are different, resulting in different intermediate data. The data anonymization rules for each version are set according to actual needs, and this embodiment of the invention does not impose any limitations.

[0070] For example, data anonymization rules may include replacing, masking, encrypting, deleting, and formatting the original data.

[0071] For example, data anonymization rules can be provided in the form of a substitution table, which includes the substitution data corresponding to the original data, such as replacing 0 with 'a', 2 with 'c', etc. The substitution table can be used to anonymize the original plaintext data to obtain intermediate data, and it can also be used to recover the original plaintext data from the intermediate data and the substitution table.

[0072] S204. According to the encryption algorithms corresponding to each preset version, each intermediate data is encrypted to obtain the desensitized data corresponding to the target data; wherein, the encryption algorithms corresponding to each version are different.

[0073] Specifically, the server encrypts intermediate data according to an encryption algorithm corresponding to a preset version, thereby obtaining one version of de-identified data corresponding to the target data. Each piece of intermediate data can be encrypted to obtain one version of de-identified data, and multiple versions of de-identified data can be obtained, with each piece of intermediate data using a different encryption algorithm. The encryption algorithm corresponding to each version is selected according to actual needs, and this embodiment of the invention does not impose any limitations.

[0074] By using different data anonymization rules and different encryption algorithms to anonymize the target data, the generated anonymized data versions are all different, which can improve the security of data use.

[0075] Based on the above embodiments, further, the step of decrypting the target data to obtain the original plaintext data corresponding to the target data includes:

[0076] Send a data decryption request to the encryption machine so that the encryption machine can decrypt the target data;

[0077] Receive the original plaintext data corresponding to the target data returned by the encryption machine.

[0078] Specifically, the server can send a data decryption request to the encryption machine, the data decryption request carrying the target data. After receiving the data decryption request, the encryption machine can decrypt the target data to obtain the original plaintext data corresponding to the target data, and then return the original plaintext data to the server. The encryption machine is specifically used for encrypting and decrypting data, and the key required to decrypt the target data can be pre-set.

[0079] Based on the above embodiments, further, the step of performing desensitization processing on the original plaintext data according to the data desensitization rules corresponding to each preset version, and generating multiple intermediate data corresponding to the target data, includes:

[0080] Send a data desensitization request to the desensitization device so that the desensitization device can desensitize the original plaintext data;

[0081] Receive multiple intermediate data corresponding to the target data returned by the desensitization device.

[0082] Specifically, the server can send a data de-identification request to the de-identification device. This request carries the original plaintext data corresponding to the target data and a de-identification template corresponding to the target data. The de-identification device can obtain data de-identification rules corresponding to each preset version based on the de-identification template. Then, based on these rules, it performs de-identification processing on the original plaintext data corresponding to the target data, obtaining multiple intermediate data corresponding to the target data. The de-identification device returns the multiple intermediate data corresponding to the target data to the server. The de-identification device is specifically designed for de-identifying target data and stores data de-identification rules corresponding to each preset version. The de-identification device can be a server.

[0083] Based on the above embodiments, further, the step of encrypting each intermediate data according to the encryption algorithm corresponding to each preset version to obtain the de-identified data corresponding to the target data includes:

[0084] Send a data encryption request to the encryption machine so that the encryption machine encrypts each intermediate data corresponding to the target data;

[0085] Receive the de-identified data corresponding to the target data returned by the encryption machine.

[0086] Specifically, the server can send a data encryption request to the encryption machine, the data encryption request carrying each intermediate data corresponding to the target data and the de-identification template corresponding to the target data. After receiving the data encryption request, the encryption machine can obtain encryption algorithms corresponding to each preset version based on the de-identification template corresponding to the target data, and then encrypt each intermediate data according to the encryption algorithms corresponding to each preset version to obtain the de-identified data corresponding to the target data. The de-identification device returns the de-identified data corresponding to the target data to the server. The encryption algorithm required to encrypt each intermediate data can be stored at the encryption machine.

[0087] By storing data anonymization rules, encryption algorithms, and target data separately, and processing the anonymization of the original plaintext data in the anonymization device and the encryption of intermediate data in the encryption machine, data security can be further improved. This avoids the risk of data leakage that would occur if data anonymization rules, encryption algorithms, and target data were all stored in one place and all anonymized and encrypted on a server.

[0088] Based on the above embodiments, the data desensitization processing method provided by the embodiments of the present invention further includes:

[0089] The data anonymization rules and / or encryption algorithms are updated according to the update rules to re-obtain the anonymized data corresponding to the target data.

[0090] Specifically, the server can update the data anonymization rules corresponding to each preset version according to the update rules, regenerate multiple intermediate data corresponding to the target data, and thus obtain the anonymized data corresponding to the target data.

[0091] The server can update the encryption algorithm corresponding to each preset version according to the update rules, and re-obtain the de-identified data corresponding to the target data.

[0092] The server can update the data anonymization rules and encryption algorithms corresponding to each preset version according to the update rules. Based on the updated data anonymization rules corresponding to each preset version, the server performs anonymization processing on the original plaintext data and regenerates multiple intermediate data corresponding to the target data. Then, based on the updated encryption algorithms corresponding to each preset version, the server encrypts each regenerated intermediate data to obtain the anonymized data corresponding to the target data.

[0093] The update rules are set according to actual needs, and this embodiment of the invention does not impose any limitations. For example, the update rules may be periodic. The periodicity is set according to actual needs, and this embodiment of the invention does not impose any limitations. By updating the data anonymization rules and / or encryption algorithms through update rules, it is possible to prevent the cracking of the anonymized data corresponding to the target data and improve data security.

[0094] Based on the above embodiments, the data desensitization processing method provided by the embodiments of the present invention further includes:

[0095] The data anonymization rules and / or encryption algorithms are updated according to the update instruction to re-obtain the anonymized data corresponding to the target data. Specifically, when the target data is at risk of leakage or being cracked, an update instruction can be sent to the server. After receiving the update instruction, the server will update the data anonymization rules and / or encryption algorithms according to the update instruction to re-obtain the anonymized data corresponding to the target data. The update instruction can be set according to actual needs, and this embodiment of the invention does not impose any limitations.

[0096] For example, the server can update the data anonymization rules corresponding to each preset version according to the update command, regenerate multiple intermediate data corresponding to the target data, and thus obtain the anonymized data corresponding to the target data.

[0097] For example, the server can update the encryption algorithm corresponding to each preset version according to the update command, and re-obtain the de-identified data corresponding to the target data.

[0098] For example, the server can update the data anonymization rules and encryption algorithms corresponding to each preset version according to the update command. Based on the updated data anonymization rules corresponding to each preset version, the server can perform anonymization processing on the original plaintext data and regenerate multiple intermediate data corresponding to the target data. Then, based on the updated encryption algorithms corresponding to each preset version, the server can encrypt each regenerated intermediate data to obtain the anonymized data corresponding to the target data.

[0099] Based on the above embodiments, the data desensitization processing method provided by the embodiments of the present invention further includes:

[0100] If the unused duration of the available business data exceeds a preset duration, the target data is archived, and the available business data is deleted from the second storage area.

[0101] Specifically, as time goes on, the amount of stored sensitive information increases, and the processing time for target data becomes longer. Therefore, data that is rarely used can be archived. The server can record the usage time of the business-available data of the target data in the second storage area each time. Based on the time interval between the last usage time and the current time, the server calculates the unused duration of the business-available data of the target data. The unused duration is compared with a preset duration. If the unused duration is greater than the preset duration, the target data can be archived and the business-available data of the target data can be deleted from the second storage area.

[0102] Archiving the target data may include storing the target data in a third storage area. Data stored in the third storage area will not be copied to the second storage area for use, but may be copied to the first storage area, and will not be updated periodically. Furthermore, to facilitate subsequent use of the data in the third storage area and to ensure data security, a corresponding de-identification processing method can be set for the third storage area. After de-identifying the target data using the method corresponding to the third storage area, the archived data corresponding to the target data is stored in the third storage area. It is understood that the de-identification processing method for the third storage area is different from the de-identification processing method used for the target data stored in the first storage area, and the data stored in the third storage area can use a unified de-identification processing method.

[0103] When it is necessary to use target data stored in the third storage area, the archived data corresponding to the target data must be decrypted first to obtain the target data before it can be used.

[0104] Figure 3 This is a flowchart illustrating the data desensitization processing method provided in the third embodiment of the present invention, as shown below. Figure 3 As shown, based on the above embodiments, further, the step of extracting data features from the target data to obtain the original feature information corresponding to the target data includes:

[0105] S301. Based on the sensitive data type of the target data, obtain the configuration information table corresponding to the target data; wherein, the correspondence between the sensitive data type and the configuration information table is preset;

[0106] Specifically, the target data has a sensitive data type, and the configuration information table corresponding to the target data can be retrieved based on the sensitive data type. The correspondence between the sensitive data type and the configuration information table is preset; the configuration information table is preset and can be set according to actual needs, but this embodiment of the invention does not impose limitations.

[0107] Table 1 Configuration Information Table

[0108]

[0109] For example, the sensitive data type of an ID card is Y, and the configuration information table corresponding to sensitive data type Y is shown in Table 1. The original feature information includes general feature information and specific feature information. General feature information includes field length, number of different characters, and character statistics; specific feature information includes region code, date of birth, and gender.

[0110] S302. Decrypt the target data to obtain the original plaintext data corresponding to the target data;

[0111] Specifically, in order to prevent data leakage, the target data is usually stored in encryption. In order to extract data features, the target data needs to be decrypted to obtain the original plaintext data corresponding to the target data.

[0112] S303. Obtain the original feature information corresponding to the target data based on the original plaintext data corresponding to the target data and the configuration information table.

[0113] Specifically, the server extracts the original feature information corresponding to the target data from the original plaintext data corresponding to the target data according to the configuration information table.

[0114] For example, for an ID card, the character length of the ID card, the number of different characters included in the ID card, and the number of each type of character included in the ID card are extracted from Table 1 as the general feature information of the ID card; the first 6 digits of the ID card are extracted as the regional code, the 7th to 14th digits are extracted as the birth date, and the gender is extracted based on the gender digit to obtain the unique feature information of the ID card.

[0115] Based on the above embodiments, the de-identified data further includes at least one of identification information, version information, encrypted identification information, and verification information.

[0116] Specifically, the identification information is the original data type identifier, used to distinguish whether it is de-identified data. The version information indicates the version of the de-identified data, used to distinguish different versions of de-identified data; each version of de-identified data has different version information. The encryption identification information is the target data after de-identification processing, such as target data processed by data de-identification rules and encryption algorithms. The verification information is used to verify the legality of the corresponding version of de-identified data.

[0117] For example, such as Figure 4As shown, the first four digits (TA10) of this version of the de-identified data are identification information, representing the original data type identifier, used to distinguish whether it is a de-identified marker; digits 4-8 (V110) are version information, used to identify the version of the de-identified data. The CDEFQEWEESDFEWA between the version information and the verification information is the encryption identification information, i.e., the target data after de-identification processing. The last two digits (FF) are verification information, used to verify the legality of this version of the de-identified data.

[0118] In this application, sensitive data can be categorized according to its lifecycle: near-line sensitive data, offline sensitive data, and archived sensitive data. The processing logic for target data differs at each stage, complementing each other to ensure that users are unaware of changes to the de-identification labeling information when using the target data.

[0119] Near-line sensitive data refers to sensitive data collected in real-time or offline from external sources. After collection, the near-line sensitive data is processed using the data de-identification processing method provided in this embodiment of the invention. The de-identified data and the corresponding original feature information are stored in the first storage area. Then, a version of the de-identified data and the corresponding original feature information are selected from the de-identified data and stored in the second storage area as the business-usable data of the near-line sensitive data.

[0120] Offline sensitive data refers to sensitive data that has already been collected and stored; specifically, it refers to sensitive data for which anonymized data has already been generated. When updating the anonymized data in the business-available data of near-line sensitive data in the second storage area based on the anonymized data corresponding to the near-line sensitive data stored in the first storage area, all versions of the anonymized data in the initially obtained near-line sensitive data will be exhausted. For offline sensitive data, the anonymization processing method will be updated according to the update rules, such as updating the data anonymization rules and / or encryption algorithms, and regenerating the corresponding anonymized data. Offline sensitive data can also have its anonymization processing method updated according to update instructions to obtain the corresponding anonymized data again.

[0121] As time goes by and business expands, the amount of stored sensitive data increases, leading to longer and more time-consuming regeneration of de-identified data from offline sensitive data. Therefore, offline sensitive data that has not been used for a long time in the first storage area can be archived to reduce the amount of offline sensitive data that needs to be regenerated. Archived sensitive data is sensitive data stored in the third storage area (archive library), determined by the business-usable data of offline sensitive data that has not been used for a preset period in the second storage area. For offline sensitive data that needs to be archived, the de-identification processing method corresponding to the third storage area can be used to de-identify the offline sensitive data, generating archived de-identified data corresponding to the offline sensitive data. The archived de-identified data and the original feature information of the offline sensitive data are stored in the third storage area. Then, the de-identified data and the corresponding original feature information of the offline sensitive data are deleted from the first storage area, and the business-usable data and the original feature information of the offline sensitive data are deleted from the second storage area. At this point, the offline sensitive data becomes archived sensitive data.

[0122] When archived sensitive data is needed, the de-identified archived version corresponding to the archived sensitive data can be obtained from the third storage area. This de-identified data is then extracted to retrieve the archived sensitive data itself. The current de-identification processing method for near-line sensitive data is then applied to the archived sensitive data to obtain the corresponding de-identified data. The de-identified data and the original feature information of the archived sensitive data are then stored in the first storage area. At this point, the archived sensitive data is transformed into offline sensitive data.

[0123] The data anonymization method provided in this invention classifies sensitive information into near-line sensitive data, offline sensitive data, and archived sensitive data according to the data lifecycle. When applied in the big data field, it can dynamically update the anonymized data of various versions corresponding to the target data in the first storage area and dynamically update the business-usable data of the target data stored in the second storage area, thereby improving data security.

[0124] Figure 5 This is a schematic diagram of the data desensitization processing device provided in the fifth embodiment of the present invention, as shown below. Figure 5 As shown, the data desensitization processing device provided in this embodiment of the invention includes a desensitization processing unit 501, a feature extraction unit 502, a first storage processing unit 503, and a second storage processing unit 504, wherein:

[0125] The de-identification processing unit 501 is used to de-identify the target data to obtain de-identified data corresponding to the target data; wherein, the de-identified data includes multiple versions, and each version of the de-identified data adopts a different de-identification processing method; the feature extraction unit 502 is used to extract data features from the target data to obtain the original feature information corresponding to the target data; the first storage processing unit 503 is used to store the de-identified data corresponding to the target data and the original feature information corresponding to the target data in a first storage area; the first storage area cannot be accessed by business applications; the second storage processing unit 504 is used to select one version of de-identified data from the de-identified data corresponding to the target data in the first storage area for the target data, and store the selected de-identified data and the original feature information corresponding to the target data as business-usable data of the target data in a second storage area; the second storage area can be accessed by business applications to realize the de-identified use of the target data in business scenarios.

[0126] Specifically, the desensitization processing unit 501 acquires the target data and performs desensitization processing on the target data to obtain desensitized data corresponding to the target data. The desensitized data corresponding to the target data includes multiple versions of desensitized data, each version employing a different desensitization processing method. The target data contains sensitive information and requires desensitization processing to protect this sensitive data. The desensitization processing method is set according to actual needs, and this embodiment of the invention does not impose limitations. In this embodiment of the invention, a version of desensitized data can also be referred to as a desensitization marker information.

[0127] The feature extraction unit 502 performs feature extraction on the target data to obtain the original feature information corresponding to the target data. The original feature information includes, but is not limited to, text features, temporal features, frequency domain features, convolutional features, etc., and can be set according to actual needs; this embodiment of the invention does not impose any limitations.

[0128] After obtaining the de-identified data and the original feature information corresponding to the target data, the first storage processing unit 503 can store the de-identified data and the original feature information corresponding to the target data in a first storage area. The data stored in the first storage area is not publicly accessible and cannot be accessed by business applications.

[0129] For the target data, the second storage processing unit 504 selects a version of de-identified data from the de-identified data corresponding to the target data in the first storage area, and uses the selected de-identified data and the original feature information corresponding to the target data as the business-usable data of the target data, storing the business-usable data of the target data in the second storage area. The data stored in the second storage area can be publicly accessed and used by business applications to achieve de-identified use of the target data in business scenarios. The first storage area and the second storage area are isolated.

[0130] The data anonymization processing apparatus provided in this embodiment of the invention can anonymize target data to obtain anonymized data corresponding to the target data. The anonymized data includes multiple versions, each employing a different anonymization processing method. Data features are extracted from the target data to obtain the original feature information corresponding to the target data. The anonymized data and the original feature information corresponding to the target data are stored in a first storage area, which is inaccessible to business applications. For the target data, one version of the anonymized data is selected from the anonymized data in the first storage area, and the selected anonymized data and the original feature information corresponding to the target data are stored as business-usable data of the target data in a second storage area. The second storage area is accessible to business applications to enable anonymized use of the target data in business scenarios. Because multiple versions of anonymized data are generated and the anonymization methods of each version differ, different versions of anonymized data can be provided for external use. Furthermore, the business-usable data of the target data is stored separately from the anonymized data corresponding to the target data, improving data security.

[0131] Figure 6 This is a schematic diagram of the data desensitization processing device provided in the sixth embodiment of the present invention, as shown below. Figure 6 As shown, the data desensitization processing apparatus provided in this embodiment of the invention further includes an update unit 505, wherein:

[0132] The update unit 505 is used to update the de-identified data corresponding to the target data stored in the first storage area and update the de-identified data in the service-available data of the target data in the second storage area.

[0133] Figure 7 This is a schematic diagram of the data desensitization processing device provided in the seventh embodiment of the present invention, as shown below. Figure 7 As shown, based on the above embodiments, the desensitization processing unit 501 further includes a first obtaining subunit 5011, a first decryption subunit 5012, a generating subunit 5013, and an encryption subunit 5014, wherein:

[0134] The first obtaining subunit 5011 is used to obtain a de-identification template corresponding to the target data based on the sensitive data type of the target data; wherein, the correspondence between the sensitive data type and the de-identification template is preset; the first decryption subunit 5012 is used to decrypt the target data to obtain the original plaintext data corresponding to the target data; the generation subunit 5013 is used to perform de-identification processing on the original plaintext data according to the data de-identification rules corresponding to each preset version, and generate multiple intermediate data corresponding to the target data; wherein, the data de-identification rules corresponding to each version are different; the encryption subunit 5014 is used to encrypt each intermediate data according to the encryption algorithm corresponding to each preset version, and obtain the de-identified data corresponding to the target data; wherein, the encryption algorithms corresponding to each version are different.

[0135] Figure 8 This is a schematic diagram of the data desensitization processing device provided in the eighth embodiment of the present invention, as shown below. Figure 8 As shown, based on the above embodiments, the data desensitization processing apparatus provided in this embodiment of the invention further includes a data re-acquisition unit 506, wherein:

[0136] The re-acquisition unit 506 is used to update the data desensitization rules and / or encryption algorithm according to the update rules and / or update instructions, so as to re-acquire the desensitized data corresponding to the target data.

[0137] Figure 9 This is a schematic diagram of the data desensitization processing device provided in the ninth embodiment of the present invention, as shown below. Figure 9 As shown, based on the above embodiments, the data desensitization processing apparatus provided in this embodiment of the invention further includes an archiving unit 507, wherein:

[0138] The archiving unit 507 is used to archive the target data and delete the service-available data from the second storage area when the unused duration of the service-available data exceeds a preset duration.

[0139] Figure 10 This is a schematic diagram of the data desensitization processing device provided in the tenth embodiment of the present invention, as shown below. Figure 10 As shown, based on the above embodiments, the feature extraction unit 502 further includes a second obtaining subunit 5021, a second decryption subunit 5022, and a third obtaining subunit 5023, wherein:

[0140] The second obtaining subunit 5021 is used to obtain a configuration information table corresponding to the target data based on the sensitive data type of the target data; wherein, the correspondence between the sensitive data type and the configuration information table is preset; the second decryption subunit 5022 is used to decrypt the target data to obtain the original plaintext data corresponding to the target data; the third obtaining subunit 5023 is used to obtain the original feature information corresponding to the target data based on the original plaintext data corresponding to the target data and the configuration information table.

[0141] Based on the above embodiments, the de-identified data further includes at least one of identification information, version information, encrypted identification information, and verification information.

[0142] The embodiments of the device provided in this invention can be used to execute the processing flow of the above-described method embodiments. Its functions will not be repeated here, but can be referred to the detailed description of the above-described method embodiments.

[0143] Figure 11 This is a schematic diagram of the physical structure of the electronic device provided in the eleventh embodiment of the present invention, as shown below. Figure 11 As shown, the electronic device may include a processor 1101, a communications interface 1102, a memory 1103, and a communication bus 1104, wherein the processor 1101, the communications interface 1102, and the memory 1103 communicate with each other via the communication bus 1104. The processor 1101 can call logical instructions in the memory 1103 to execute the data desensitization processing method described in any of the above embodiments.

[0144] Furthermore, the logical instructions in the aforementioned memory 1103 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0145] This embodiment discloses a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by a computer, the computer can execute the data desensitization processing method provided in the above-described method embodiments.

[0146] This embodiment provides a computer-readable storage medium storing a computer program that causes the computer to execute the data desensitization processing method provided in the above-described method embodiments.

[0147] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0148] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0149] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0150] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0151] In the description of this specification, the references to terms such as "an embodiment," "a specific embodiment," "some embodiments," "for example," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0152] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A data anonymization processing method, characterized in that, include: The target data is anonymized to obtain anonymized data corresponding to the target data; wherein, the anonymized data includes multiple versions, and each version of the anonymized data uses a different anonymization processing method; Data feature extraction is performed on the target data to obtain the original feature information corresponding to the target data; The de-identified data corresponding to the target data and the original feature information corresponding to the target data are stored in the first storage area; the first storage area cannot be accessed by business applications. For the target data, select one version of the de-identified data from the de-identified data corresponding to the target data in the first storage area, and store the selected de-identified data and the original feature information corresponding to the target data as the business-usable data of the target data in the second storage area; the second storage area can be accessed by business applications to realize the de-identified use of the target data in business scenarios.

2. The method according to claim 1, characterized in that, Also includes: Based on the de-identified data corresponding to the target data stored in the first storage area, update the de-identified data in the service-available data of the target data in the second storage area.

3. The method according to claim 1, characterized in that, The process of de-identifying the target data to obtain the de-identified data corresponding to the target data includes: Based on the sensitive data type of the target data, a de-identification template corresponding to the target data is obtained; wherein, the correspondence between the sensitive data type and the de-identification template is preset; The target data is decrypted to obtain the original plaintext data corresponding to the target data; According to the data anonymization rules corresponding to each preset version, the original plaintext data is anonymized to generate multiple intermediate data corresponding to the target data; wherein the data anonymization rules corresponding to each version are different. Each intermediate data is encrypted according to the encryption algorithm corresponding to each preset version, to obtain the de-identified data corresponding to the target data; wherein the encryption algorithms corresponding to each version are different.

4. The method according to claim 3, characterized in that, Also includes: Update the data anonymization rules and / or encryption algorithms according to the update rules and / or update instructions to re-obtain the anonymized data corresponding to the target data.

5. The method according to claim 1, characterized in that, Also includes: If the unused duration of the available business data exceeds a preset duration, the target data is archived, and the available business data is deleted from the second storage area.

6. The method according to claim 1, characterized in that, The step of extracting data features from the target data to obtain the original feature information corresponding to the target data includes: Based on the sensitive data type of the target data, a configuration information table corresponding to the target data is obtained; wherein, the correspondence between the sensitive data type and the configuration information table is preset; The target data is decrypted to obtain the original plaintext data corresponding to the target data; Based on the original plaintext data corresponding to the target data and the configuration information table, the original feature information corresponding to the target data is obtained.

7. The method according to any one of claims 1 to 6, characterized in that, The de-identified data includes at least one of the following: identification information, version information, encrypted identification information, and verification information.

8. A data desensitization processing device, characterized in that, include: The desensitization processing unit is used to desensitize the target data to obtain desensitized data corresponding to the target data; wherein, the desensitized data includes multiple versions, and each version of the desensitized data adopts a different desensitization processing method; The feature extraction unit is used to extract data features from the target data to obtain the original feature information corresponding to the target data. The first storage processing unit is used to store the de-identified data corresponding to the target data and the original feature information corresponding to the target data into a first storage area; the first storage area cannot be accessed by business applications. The second storage processing unit is used to select a version of de-identified data from the de-identified data corresponding to the target data in the first storage area for the target data, and store the selected de-identified data and the original feature information corresponding to the target data as the business-usable data of the target data in the second storage area; the second storage area can be accessed by business applications to realize the de-identified use of the target data in business scenarios.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 7.

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