Data processing method and platform, electronic equipment and storage medium

Abstract

The invention relates to the field of data processing, and discloses a data processing method and platform, electronic equipment and a storage medium, and the method comprises the steps: determining related situation features of a data query request; the data query request comprises a to-be-queried data field; the related situation feature refers to a feature associated with a data query scene and a service demand of the data query request; determining a sensitivity level label corresponding to the data field according to the related situation characteristics; determining a desensitization rule corresponding to the data field according to the sensitivity level label and the related situation feature; and performing desensitization processing on the target data corresponding to the data field according to the desensitization rule to obtain desensitized data. According to the method, the sensitivity level label is determined through the related situation characteristics, dynamic authority management is realized, scene differences can be accurately adapted according to the desensitization rule determined according to the sensitivity level label and the related situation characteristics, the data access authority can be accurately regulated and controlled according to actual service requirements, and the availability of the desensitization data is improved.

Description

Technical Field

[0001] This application relates to the field of data processing, specifically to a data processing method, platform, electronic device, and storage medium. Background Technology

[0002] Enterprise master data, as a core asset, needs to be frequently synchronized to downstream business systems. Currently, traditional solutions distribute anonymized data to downstream business systems through pre-defined data anonymization rules to achieve data synchronization. However, this method of anonymizing data through pre-defined rules can easily lead to the loss of business usability of the anonymized data, reducing its availability. Summary of the Invention

[0003] In view of the above problems, embodiments of this application provide a data processing method, platform, electronic device, and storage medium to solve the problem in related technologies that desensitizing data through preset data desensitization rules reduces the usability of desensitized data.

[0004] According to one aspect of the embodiments of this application, a data processing method is provided, the method comprising: Determine the relevant contextual features of the data query request; the data query request includes the data fields to be queried; the relevant contextual features refer to the features associated with the data query scenario and business requirements of the data query request. Determine the sensitivity level label corresponding to the data field based on the relevant contextual characteristics; Decode the desensitization rules corresponding to the data fields based on the sensitivity level labels corresponding to the data fields and the relevant contextual features; The target data corresponding to the data field is desensitized according to the desensitization rules to obtain the desensitized data corresponding to the target data.

[0005] According to another aspect of the embodiments of this application, a data processing platform is provided, including: The first processing module is used to determine the relevant contextual features of the data query request; the data query request includes the data field to be queried; the relevant contextual features refer to the features associated with the data query scenario and business requirements of the data query request. The second processing module is used to determine the sensitivity level label corresponding to the data field based on the relevant contextual characteristics. The third processing module is used to determine the desensitization rule corresponding to the data field based on the sensitivity level label corresponding to the data field and the relevant contextual features; The fourth processing module is used to perform desensitization processing on the target data corresponding to the data field according to the desensitization rules, so as to obtain the desensitized data corresponding to the target data.

[0006] According to another aspect of the embodiments of this application, an electronic device is provided, including: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; the memory is used to store at least one executable instruction, which causes the processor to perform some or all of the steps in the above method.

[0007] According to another aspect of the embodiments of this application, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements some or all of the steps in the above-described method.

[0008] This application provides a comprehensive basis for dynamic decision-making on data permissions and desensitization rules by determining the relevant contextual characteristics of data query requests. Based on the relevant contextual characteristics of data query requests, the sensitivity level labels of data fields can be determined in a targeted manner, breaking the limitations of traditional fixed permission allocation. This allows data sensitivity determination to accurately match real-time business scenarios, avoiding a disconnect between permission control and actual needs, thereby improving the granularity and adaptability of data access control. Furthermore, by combining sensitivity level labels with relevant contextual characteristics to determine the desensitization rules for data fields, suitable desensitization methods can be flexibly selected, rather than using a uniform desensitization approach. This ensures that sensitive data is effectively protected in non-essential scenarios while retaining the core usability of data in specific business scenarios, thus resolving the contradiction between data security and availability. This application achieves synergistic optimization of data security and business efficiency, significantly improving the security, flexibility, and availability of data during the distribution and synchronization process to downstream business systems.

[0009] The above description is merely an overview of the technical solutions of the embodiments of this application. In order to better understand the technical means of the embodiments of this application and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of this application more obvious and understandable, specific implementation methods of this application are described below. Attached Figure Description

[0010] The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart illustrating the data processing method provided in this application is shown; Figure 2 This paper presents a schematic diagram of the overall flow of the data processing method provided in this application; Figure 3 A timing diagram of the data processing method provided in this application is shown; Figure 4 This application illustrates a basic flowchart of the data processing provided. Figure 5 A detailed flowchart illustrating the data processing provided in this application is shown. Figure 6 A schematic diagram of the structure of an embodiment of the data processing platform of this application is shown; Figure 7 A schematic diagram of an embodiment of the electronic device provided in this application is shown. Detailed Implementation

[0011] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. Although exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein.

[0012] It is understood that before using the technical solutions disclosed in the various embodiments of this application, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this application in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0013] Figure 1 A flowchart of the data processing method of this application is shown. Figure 1 As shown, the method includes the following steps: Step 110: Determine the relevant contextual characteristics of the data query request.

[0014] The data processing method of this application is applied to a data processing platform. The data processing platform can accept data query requests from downstream data requesters, perform desensitization processing on the requested master data (target data), and send the desensitized data obtained from the desensitization processing to the downstream data requesters, thereby achieving data synchronization.

[0015] In this embodiment, the data query request includes the data field to be queried. The data field to be queried refers to the data that needs to be obtained as explicitly specified in the data query request, such as user mobile phone number, email address, salary amount, transaction amount, employee ID, etc. Upon receiving a data query request from a downstream data requester, the relevant contextual characteristics of the data query request are determined.

[0016] A data query request refers to a request initiated by a downstream data requester to a data processing platform to obtain specific master data. The data query request must explicitly include the data fields to be queried, such as mobile phone number, salary, transaction amount, etc., as well as scenario information related to the query.

[0017] A data query request refers to the features associated with the data query scenario and business requirements. It represents a multi-dimensional set of information that corresponds to the data query request and reflects the query scenario or business scenario. Its core purpose is to distinguish the differences between different query scenarios.

[0018] Step 120: Determine the sensitivity level label corresponding to the data field based on the relevant contextual characteristics.

[0019] In this application embodiment, the sensitivity level label corresponding to the same data field can be different under different relevant contextual characteristics. The sensitivity level label is used to identify the classification of the sensitivity of the data field in a specific business scenario / query scenario, such as P0, P1, P2, P3, etc. The higher the level, the higher the sensitivity. Different sensitivity level labels correspond to different access control permissions and de-identification processing strengths. For example, P0 indicates complete denial of access, P1 indicates high-intensity de-identification is required, P2 indicates partial display / low-intensity de-identification is allowed, and P3 indicates complete display is allowed.

[0020] As an example, a sensitivity rating rule refers to a pre-defined logical rule used to determine the sensitivity level of a data field under different contextual characteristics. Its core is establishing a correspondence between data fields, relevant contextual characteristics, and sensitivity level labels. Different sensitivity rating rules for different data fields can be pre-defined and stored in a sensitivity rating rule library. This library stores data fields and their corresponding sensitivity rating rules in a correspondence manner, and is a database that stores all sensitivity rating rules for all data fields. Sensitivity rating rules include different sensitivity level labels for the same data field under different relevant contextual characteristics. As an example, the sensitivity rating rule corresponding to a data field can be determined, and the sensitivity level label corresponding to the data field can be determined based on the relevant contextual characteristics and the sensitivity rating rule.

[0021] Step 130: Determine the desensitization rules for the data fields based on the sensitivity level labels and relevant contextual characteristics.

[0022] In this application embodiment, the de-identification rule refers to the specific methods and operational standards for processing sensitive data. Different de-identification rules correspond to different de-identification algorithms and processing intensities. The core objective is to maximize the business availability of retained data while ensuring data security. By combining the degree of sensitivity with the needs of the scenario, an appropriate de-identification rule can be selected to ensure data security while avoiding excessive de-identification that could impair data availability.

[0023] Multiple anonymization rules can be pre-defined, such as: Tokenization: Mapping identifier fields like phone numbers and email addresses to unique virtual tokens within the system (e.g., 11100002222->V-1A2B3C), suitable for scenarios requiring external communication. Differential privacy: Adding controllable random noise to query results for numerical fields like salary and transaction amounts, suitable for statistical analysis scenarios. Reversible encryption: Encrypting data that may need to be restored, such as employee IDs, which can be decrypted with a key after authorization, suitable for troubleshooting scenarios. Partial masking: Hiding parts of the data using symbols like asterisks (e.g., phone number 111****2222), suitable for regular queries that don't require complete data.

[0024] This implementation allows for the selection of appropriate data anonymization rules from various options based on the sensitivity level labels and relevant contextual characteristics of the data fields. By employing a filtering logic that combines sensitivity level labels and relevant contextual characteristics, it breaks away from the traditional, singular, and fixed allocation model of anonymization rules. This enables precise matching of data fields to the security and business needs of specific scenarios. On one hand, sensitivity level labels ensure that the anonymization strength matches the data's sensitivity level, avoiding insufficient protection of sensitive data or excessive anonymization of non-sensitive data. On the other hand, by combining relevant contextual characteristics to select the most suitable anonymization rule for the scenario, it avoids the data usability loss problem caused by traditional fixed anonymization rules. It also achieves dynamic adaptation and precise control of anonymization rules, ultimately ensuring data security while maximizing the retention of data's business utility value, significantly improving the security, flexibility, and adaptability of enterprise master data distribution and synchronization.

[0025] Step 140: Perform desensitization processing on the target data corresponding to the data field according to the desensitization rules to obtain the desensitized data corresponding to the target data.

[0026] In this embodiment of the application, after obtaining the de-identified data corresponding to the target data, the data processing method further includes: sending the de-identified data corresponding to the target data to the downstream data requester. This transforms the original data into secure and usable de-identified data, protecting sensitive information while meeting the business needs of the downstream system.

[0027] In this application embodiment, target data refers to the original enterprise owner data corresponding to the data field to be queried, that is, real data that has not undergone any de-identification processing, such as real mobile phone number 11100002222, real salary of 8000 yuan / month, real transaction amount of 5000 yuan, etc.

[0028] Desensitization refers to the process of desensitizing target data according to the desensitization rules determined in step 130. The core principle of desensitization is to mask sensitive information while ensuring that the data can be used normally.

[0029] De-identified data is data obtained by de-identifying the target data and can be securely sent to downstream data requesters. This de-identified data neither leaks the original sensitive information nor fails to meet the business needs of downstream data requesters.

[0030] In one optional implementation, step 110 determines the relevant contextual characteristics of the data query request, including: Step 1101: Upon receiving a data query request from a downstream data requester, determine the user type of the user to which the data field belongs, the business intent corresponding to the data query request, and the environmental attributes of the data query request based on the data query request.

[0031] Step 1102: Determine the user type of the user to which the data field belongs, the business intent corresponding to the data query request, and the environmental attributes of the data query request as the relevant contextual features of the data query request.

[0032] In this embodiment, the downstream data requester refers to various business systems that need to access the enterprise's master data, such as customer service systems that handle user services, store systems that are responsible for offline operations, and OA systems that facilitate collaborative office work. Upon receiving a data query request from the downstream data requester, the system obtains the context information of the data query request and determines relevant contextual features based on the context information.

[0033] The user type of the data field refers to the user type (i.e., attribute classification) of the user (i.e., the data subject) to which the data field to be queried belongs. For example, the user type could be senior executive, ordinary employee, VIP user, or ordinary user. If the user to which the data field to be queried belongs is employee A, and employee A is a senior executive, then employee A's user type is senior executive.

[0034] The business intent behind a data query request refers to the specific purpose or goal of the downstream data requester initiating the data query. For example, a "fault handling" work order initiated by a customer service system. Specific business intents include emergency roadside assistance, emergency fault handling, routine business inquiries, customer satisfaction follow-ups, financial statistical analysis, and system fault troubleshooting. Different business intents have different requirements for data completeness and availability. For example, emergency roadside assistance requires complete contact information, while financial statistical analysis only requires the statistical value of the data and does not require individual real information.

[0035] The environmental attributes of a data query request refer to the characteristics of the context in which the data query request is initiated, directly reflecting the environmental risk level of data access. Environmental attributes may include the time the request is initiated (e.g., during working hours on weekdays or during off-peak hours in the early morning), the IP address of the request initiator (e.g., company intranet IP or external public network IP), geographical location (e.g., the company's registered location or an unfamiliar city), and network security (e.g., whether access is through a trusted corporate VPN or a public Wi-Fi network).

[0036] In an optional implementation, step 120 determines the sensitivity level label corresponding to the data field based on relevant contextual characteristics, including: Step 1201: Determine the rating rules corresponding to the data fields.

[0037] Step 1202: Determine the sensitivity level label corresponding to the data field based on the relevant contextual characteristics and the rating rules corresponding to the data field.

[0038] In this embodiment, based on preset grading rules, the sensitivity level is standardized and automatically determined, avoiding the subjectivity of manual judgment and improving the efficiency and accuracy of sensitivity determination.

[0039] For example, when the data field is a mobile phone number: 1. If the user type is a VIP customer, the business intent is emergency roadside assistance, the environment is a company intranet IP, and the time period is working hours, the corresponding sensitivity level label is P2. 2. If the user type is a regular user, the business intent is satisfaction follow-up, the environment is an external public IP, and the time period is not working hours, the corresponding sensitivity level label is P1. 3. If the environment is an unfamiliar IP from a different location and access is not through a VPN, regardless of the user type or business intent, the sensitivity level label is P0.

[0040] The relevant contextual characteristics of the determined data query request are: the data field is a mobile phone number, the user type is a regular user, the business intent is a satisfaction follow-up, the environmental attribute is an external public IP, and it is outside working hours. Therefore, based on the relevant contextual characteristics and the corresponding rating rules for the data field, the sensitivity level label for the data field is determined to be P1.

[0041] For example, if the rating rules for a given data field are as follows: 1. If the user type is a senior executive, the business intent is financial auditing, and the environment is a trusted VPN access within the internal network, the corresponding sensitivity level label is P1. 2. If the user type is a junior employee, the business intent is personal salary inquiry, and the environment is a company internal IP address, the corresponding sensitivity level label is P2. 3. If the business intent is unauthorized external inquiry, and the environment is an external public network, the corresponding sensitivity level label is P0.

[0042] The relevant contextual characteristics of the identified data query request are: the data field is salary, the user type is a senior executive of the group, the business intent is an unauthorized external query, and the environmental attribute is an external public network. Therefore, based on the relevant contextual characteristics and the rating rules corresponding to the data field, the sensitivity level label corresponding to the data field is determined to be P0.

[0043] In an optional implementation, step 130 determines the desensitization rule corresponding to the data field based on the sensitivity level label and relevant contextual characteristics of the data field, including: Step 1301: Determine the field type corresponding to the data field and obtain the business intent in the relevant contextual features.

[0044] In the embodiments of this application, field type refers to the classification of data fields based on the functional attributes and presentation of the data.

[0045] As an example, the field type can be an identifier field type, a numeric field type, etc. An identifier field type indicates that the data field is an identifier. An identifier field type means that the data field is used to uniquely identify a real user or to establish a relationship. For example, the field type corresponding to mobile phone number, email address, ID card number, employee ID, and customer number is an identifier field type. The field type corresponding to salary amount, transaction amount, order quantity, and revenue data is a numeric field type.

[0046] Step 1302: Determine the desensitization rules for the data fields based on the sensitivity level labels, field types, and business intent in the relevant contextual features.

[0047] In an optional implementation, step 1302 determines the desensitization rule for the data field based on the sensitivity level label corresponding to the data field, the field type corresponding to the data field, and the business intent in the relevant contextual features, including: The desensitization rules obtained from the desensitization strategy library, corresponding to the sensitivity level label, field type, and business intent, will be determined as the desensitization rules for the data fields.

[0048] In this embodiment, the de-identification strategy library includes sensitivity level labels, field types, business intents, and de-identification rules stored in a mapping relationship. The de-identification strategy library can be designed and modified according to actual needs. The library includes multiple pre-set de-identification strategies, each including sensitivity level labels, field types, business intents, and de-identification rules stored in a mapping relationship. Through the mapping relationship of the de-identification strategy library, fast and standardized matching of de-identification rules is achieved, improving the efficiency and consistency of de-identification rule selection, while also supporting flexible iteration of the strategy library to adapt to more business scenarios.

[0049] As an example, a de-identification strategy could be: sensitivity level label P1, field type identifier field type (phone number), business intent customer satisfaction follow-up, and de-identification rule partial masking. For example, the phone number 11100002222 could be de-identified as 111****2222.

[0050] The sensitivity level is labeled P1, the field type is a numeric field (salary), the business intent is financial statistical analysis, and the anonymization rule is differential privacy. For example, the original department salary data of 7000, 8000, and 9000 becomes 7050, 7980, and 9020 after adding controllable noise.

[0051] The sensitivity level is labeled P2, the field type is an identifier field (phone number), the business intent is emergency roadside assistance, and the desensitization rule is tokenization. For example, the phone number 11100002222 is mapped to the virtual token V-4D5E6F.

[0052] The sensitivity level is labeled P2, the field type is an identifier field (employee ID), the business intent is system fault diagnosis, and the desensitization rule is reversible encryption. For example, employee ID: 0025 can be encrypted as ciphertext F-9A8B7C.

[0053] The sensitivity level is P0, the field type is numeric (salary), the business intent is unauthorized external query, and the de-identification rule is to completely reject all responses. The system directly sends a message to the downstream data requester stating that access is denied and does not return any salary-related data.

[0054] In an optional implementation, before performing desensitization processing on the target data corresponding to the data field according to the desensitization rules, the data processing method further includes: Generate data query statements based on sensitivity level labels and data fields.

[0055] Execute the data query statement to obtain the target data corresponding to the data field.

[0056] In this embodiment of the application, the data fields to be queried are determined to have access permissions based on the sensitivity level label, and a data query statement for the data fields with access permissions is generated.

[0057] By filtering data fields with access permissions using sensitivity level tags, data query statements are generated to ensure that only target data within the authorized scope is retrieved, reducing the risk of sensitive data exposure from the source of the query.

[0058] For example, P0 indicates complete denial of access; data fields with a sensitivity level label of P0 are data fields without access permissions. Data fields with sensitivity level labels of P1, P2, and P3 are data fields with access permissions.

[0059] In this embodiment, all enterprise master data is uniformly stored in a structured database, such as relational databases like MySQL and Oracle. If the master data storage medium is an SQL database, then the SQL database is the master database. Data query statements are SQL data query statements. For example, if the data field to be queried is a mobile phone number with a sensitivity level label of P1 (access permission allowed), the generated SQL data query statement could be: `SELECT phone FROM user_info WHERE user_id='0025'`, indicating a query for the mobile phone number of employee ID 0025. If the data field to be queried contains both mobile phone number (P1) and salary (P0), then the above query statement is generated only for the mobile phone number; the salary is not included in the query scope due to lack of access permission. After executing the generated SQL data query statement, the original data corresponding to the mobile phone number (e.g., 11100002222) is extracted from the database; this original data is the target data corresponding to the data field.

[0060] In related technologies, the main problems encountered when synchronizing enterprise master data to downstream business systems include: rigid access control, poor business agility, and the contradiction between data availability and security. Traditional methods employ static, predefined access policies (such as assigning fixed accessible fields to a specific system). This approach cannot adapt to complex and ever-changing real-time business scenarios. For example, a customer service system needs to obtain a user's complete mobile phone number when handling emergency roadside assistance, but this is unnecessary when conducting satisfaction follow-ups. Static permissions cannot achieve this scenario-based dynamic and granular management, making it difficult to balance security and efficiency. Conventional data anonymization techniques (such as hiding part of the information with asterisks *) ensure security but severely damage the business value of the data. Anonymized mobile phone numbers cannot be used for outbound calls, and anonymized values ​​cannot be used for accurate statistical analysis. Downstream business systems cannot effectively utilize the data while ensuring security. Therefore, this application aims to solve the problem of how to achieve dynamic, flexible, and fine-grained data access control based on real-time business scenarios while ensuring data security, and ensuring the availability of anonymized data in specific scenarios.

[0061] Figure 2 A schematic diagram of the overall flow of the data processing method provided in this application is shown, such as... Figure 2 As shown, the data processing platform includes an intelligent API gateway, a context-aware engine, a policy center, an intelligent data anonymization factory, and a main database. The overall data processing workflow includes: Phase 1: Determining the relevant contextual characteristics and sensitivity level labels of the data query request; Phase 2: Acquiring the target data and determining the anonymization rules; Phase 3: Distributing the anonymized data.

[0062] Phase 1: Determine the relevant contextual characteristics and sensitivity level labels for data query requests: Downstream business systems initiate data query requests. Downstream business systems (such as customer service / store systems) send data query requests to the intelligent API gateway. The data query request contains the data fields to be queried (such as mobile phone number, salary), business intent, and user type.

[0063] After receiving a data query request, the intelligent API gateway parses the data query request, obtains relevant contextual features, and forwards the relevant contextual features to the context-aware engine.

[0064] Relevant contextual characteristics: Data subject attributes (user type), such as the type of user to which the data field belongs: ordinary user / group executive.

[0065] Business intent, such as the purpose of the data query in the downstream system: satisfaction survey / unauthorized external query. Environmental attributes, such as the requested IP address and time: external public IP / off-peak hours.

[0066] The context-aware engine retrieves the rating rules corresponding to the data field from the policy center, and determines the sensitivity level label for the data field based on the relevant context features and the rating rules. The sensitivity level label is then returned to the intelligent API gateway.

[0067] Example 1: The data field is mobile phone number, and the business intent is satisfaction follow-up -> sensitivity level label P1.

[0068] Example 2: The data field is salary, the business intent is unauthorized external query -> sensitivity level label P0.

[0069] Phase Two of the Process: Obtaining Target Data and Determining De-identification Rules: The intelligent API gateway filters out data fields with access permissions (P1 / P2 / P3 indicate authorized fields, P0 indicates unauthorized fields), generates corresponding data query statements (such as SQL statements), and executes the data query statements. The main database then returns the target data.

[0070] The intelligent data masking factory matches the corresponding data masking rules from the data masking strategy library, performs data masking on the target data, obtains the masked data, and returns it to the intelligent API gateway.

[0071] Phase Three: Distribution of Anonymized Data The intelligent API gateway distributes anonymized data to downstream business systems to complete data synchronization.

[0072] Traditional permission models are static, meaning which roles (or systems) can access which data. This mapping is pre-defined and unchanging. To address the problem that static permissions cannot achieve dynamic and granular management based on scenarios, leading to a trade-off between security and efficiency, this application designs a dynamic context-aware permission model. This model can acquire relevant contextual characteristics of data query requests in real time each time a data query request is processed. The dynamic context-aware permission model also includes a real-time permission calculation engine. Relevant contextual characteristics are input into the real-time permission calculation engine (context-aware engine), which has pre-defined level assessment rules for data fields. By executing these rules, the engine determines a dynamic, temporary sensitivity level label for each data field. The access permissions for the same data field are calculated and dynamically changed in real time under different business scenarios and environments.

[0073] Figure 3 A timing diagram of the data processing method provided in this application is shown, as follows: Figure 3 As shown, the process acquires data query requests and context information. The context information includes the data subject context (the user type of the data field in the relevant contextual features), the data usage context (the business intent corresponding to the data query request), and the query environment context (the environmental attributes of the data query request). The context-aware engine performs real-time calculations to obtain field-level sensitivity level labels. The intelligent de-identification factory matches corresponding de-identification rules, such as tokenization, differential privacy, or reversible encryption. Finally, the de-identification process yields secure data that retains functionality, i.e., de-identified data.

[0074] Figure 4 This application illustrates a basic flowchart of the data processing provided, as shown below. Figure 4 As shown, the master data from the HR / master data source system is first accessed to the platform through the master data access layer. The master data core database stores various sensitivity level labels, such as P0, P1, P2, P3, etc., and also includes the master database. The strategy center stores sensitivity assessment rules, desensitization rules, etc. Sensitivity level labels, sensitivity assessment rules, desensitization rules, etc., can be forwarded through the intelligent API gateway. The intelligent desensitization factory is equipped with intelligent desensitization components corresponding to each desensitization rule. Desensitization processing is performed through these intelligent desensitization components to obtain desensitized data. Downstream data requesters can be customer service systems, store systems, OA systems, etc.

[0075] Figure 5 This application illustrates a detailed flowchart of the data processing provided, showing the master data flow and processing logic between the platform and external systems as follows: Figure 5As shown, the core is the chain of master data access, hierarchical desensitization, and intelligent distribution. Data Access: Master data from external systems (such as master data source systems) first enters the platform through the master data access layer. Data Storage and Hierarchy: The accessed master data is stored in the master database within the master data core library. Simultaneously, based on the level assessment rules of the policy center, field-level sensitivity level labels are determined. Processing and Desensitization: Data in the core library is scheduled by the intelligent API gateway and desensitized according to the desensitization rules of the policy center through the data desensitization component. Data Output: The desensitized secure data is distributed to downstream business systems (customer service systems, store systems, OA systems, etc.) through the intelligent API gateway.

[0076] Traditional data masking aims to make data unidentifiable, often at the expense of data usability. This application proposes a dynamic data masking rule selection mechanism, which determines the corresponding data masking rule for a data field based on its sensitivity level label and relevant contextual characteristics. The goal of this dynamic mechanism is to maximize the business value of the data while ensuring security. Specifically, this is achieved by maintaining a data masking strategy library. This library does not simply map field types and masking methods, but rather maps field types, sensitivity level labels, business intent, and optimal masking rules in a more refined way.

[0077] By determining the sensitivity level label of the data field based on the relevant contextual characteristics of the data query request, it is possible to determine in real time what level of data should be allowed to be seen in the current scenario.

[0078] The sensitivity level label is passed to the intelligent de-identification algorithm factory. The intelligent de-identification algorithm factory determines the technical method to implement it based on what should be seen, and selects the de-identification rule that best balances security and business function requirements. That is, it determines the de-identification rule for the data field based on the sensitivity level label corresponding to the data field and the relevant contextual characteristics.

[0079] By identifying the relevant contextual characteristics of data query requests, determining the sensitivity level labels corresponding to data fields based on these characteristics, and then applying the desensitization rules corresponding to the data fields in a coordinated manner based on the sensitivity level labels and relevant contextual characteristics, the platform no longer mechanically executes pre-set rules. Instead, it can make targeted and optimized security and efficiency balance decisions for each specific data query request, thereby solving the problem of balancing data security and data availability.

[0080] The technical solution of this application can realize dynamic and contextualized permissions. Unlike the static and predefined permissions in the prior art, this invention realizes real-time and adaptive calculation of permissions, enabling data permissions to accurately match changing business needs and greatly releasing the value of data while ensuring security.

[0081] The technical solution of this application can realize intelligent and functional de-identification, avoid the use of simple hiding de-identification methods, and by introducing advanced algorithms such as tokenization and differential privacy, the data can still maintain its usability in specific scenarios after de-identification, thus solving the problem of the difficulty in achieving both data security and utility.

[0082] The technical solution of this application achieves the technical effect of dynamically optimizing and desensitizing data on demand through the synergistic effect of dynamic perception and intelligent desensitization.

[0083] Figure 6 A schematic diagram of the structure of an embodiment of the data processing platform of this application is shown. Figure 6 As shown, the data processing platform 300 includes: The first processing module 310 is used to determine the relevant contextual characteristics of the data query request. The data query request includes the data fields to be queried. Relevant contextual characteristics refer to the features associated with the data query scenario and business requirements of the data query request; The second processing module 320 is used to determine the sensitivity level label corresponding to the data field based on the relevant contextual characteristics.

[0084] The third processing module 330 is used to determine the desensitization rules corresponding to the data fields based on the sensitivity level labels and relevant contextual characteristics of the data fields.

[0085] The fourth processing module 340 is used to perform desensitization processing on the target data corresponding to the data field according to the desensitization rules, so as to obtain the desensitized data corresponding to the target data.

[0086] Among them, the first processing module 310 corresponds to the intelligent API gateway, the second processing module 320 corresponds to the context-aware engine, and the third processing module 330 and the fourth processing module 340 correspond to the intelligent de-identification factory.

[0087] In one optional implementation, the first processing module 310 includes: The first processing unit is used to determine the user type of the user to which the data field belongs, the business intent corresponding to the data query request, and the environmental attributes of the data query request based on the data query request when it receives a data query request sent by a downstream data requester.

[0088] The second processing unit is used to determine the user type of the user to which the data field belongs, the business intent corresponding to the data query request, and the environmental attributes of the data query request as the relevant contextual features of the data query request.

[0089] In one optional implementation, the third processing module 330 includes: The third processing unit is used to determine the field type corresponding to the data field and obtain the business intent in the relevant contextual features.

[0090] The fourth processing unit is used to determine the desensitization rules for the data fields based on the sensitivity level labels, field types, and business intent in the relevant contextual features.

[0091] In one optional implementation, the fourth processing unit is specifically configured to determine the de-identification rules corresponding to sensitivity level labels, field types, and business intents obtained from the de-identification strategy library as the de-identification rules corresponding to the data fields. The de-identification strategy library includes sensitivity level labels, field types, business intents, and de-identification rules stored in a mapping relationship. The de-identification strategy library is stored in the strategy center.

[0092] In one optional implementation, the second processing module 320 is specifically used to determine the rating rules corresponding to the data field. Based on relevant contextual characteristics and the rating rules corresponding to the data field, a sensitivity level label corresponding to the data field is determined. The rating rules are stored in the policy center.

[0093] In an optional implementation, the data processing platform 300 further includes a fifth processing module, used to generate a data query statement based on the sensitivity level label and the data field before performing desensitization processing on the target data corresponding to the data field according to the desensitization rules.

[0094] Execute the data query statement to obtain the target data corresponding to the data field.

[0095] The fifth processing module corresponds to the intelligent API gateway. Target data is stored in the main database.

[0096] In an optional implementation, the data processing platform 300 further includes a sixth processing module, used to send the de-identified data corresponding to the target data to the downstream data requester after obtaining the de-identified data corresponding to the target data.

[0097] The sixth processing module also corresponds to the intelligent API gateway.

[0098] Figure 7 The diagram shows a structural schematic of an embodiment of the electronic device of this application. The specific embodiments of this application do not limit the specific implementation of the electronic device.

[0099] like Figure 7 As shown, the electronic device may include: a processor 402, a communications interface 404, a memory 406, and a communications bus 408.

[0100] The processor 402, communication interface 404, and memory 406 communicate with each other via communication bus 408. Communication interface 404 is used to communicate with other network elements such as clients or other servers. The processor 402 executes program 410, specifically performing the relevant steps described above in the data processing method embodiment.

[0101] Specifically, program 410 may include program code, which includes computer-executable instructions.

[0102] Processor 402 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. The electronic device includes one or more processors, which may be processors of the same type, such as one or more CPUs, or processors of different types, such as one or more CPUs and one or more ASICs.

[0103] Memory 406 is used to store program 410. Memory 406 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0104] This application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the data processing method described in any of the above method embodiments.

[0105] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Furthermore, the embodiments in this application are not directed to any particular programming language.

[0106] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. Similarly, for the purpose of simplification and aiding understanding of one or more aspects of the invention, in the above description of exemplary embodiments of this application, various features of the embodiments are sometimes grouped together in a single embodiment, figure, or description thereof. The claims, which follow the detailed description, are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this application.

[0107] Those skilled in the art will understand that the modules in the device of the embodiment can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiment can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components, except that at least some of such features and / or processes or units are mutually exclusive.

[0108] It should be noted that the above embodiments are illustrative of this application and not restrictive, and those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. Unless otherwise specified, the steps in the above embodiments should not be construed as limiting the order of execution.

Claims

1. A data processing method, characterized in that, The method includes: Determine the relevant contextual features of the data query request; the data query request includes the data fields to be queried; the relevant contextual features refer to the features associated with the data query scenario and business requirements of the data query request. Determine the sensitivity level label corresponding to the data field based on the relevant contextual characteristics; Decode the desensitization rules corresponding to the data fields based on the sensitivity level labels corresponding to the data fields and the relevant contextual features; The target data corresponding to the data field is desensitized according to the desensitization rules to obtain the desensitized data corresponding to the target data.

2. The method according to claim 1, characterized in that, The relevant contextual features for determining the data query request include: Upon receiving the data query request from the downstream data requester, the user type of the user to which the data field belongs, the business intent corresponding to the data query request, and the environmental attributes of the data query request are determined based on the data query request. The user type of the user to which the data field belongs, the business intent corresponding to the data query request, and the environmental attributes of the data query request are determined as the relevant contextual features of the data query request.

3. The method according to claim 2, characterized in that, The step of determining the desensitization rule corresponding to the data field based on the sensitivity level label corresponding to the data field and the relevant contextual features includes: Determine the field type corresponding to the data field, and obtain the business intent from the relevant contextual features; The desensitization rules for the data fields are determined based on the sensitivity level labels corresponding to the data fields, the field types corresponding to the data fields, and the business intent in the relevant contextual features.

4. The method according to claim 3, characterized in that, The step of determining the desensitization rule corresponding to the data field based on the sensitivity level label corresponding to the data field, the field type corresponding to the data field, and the business intent in the relevant contextual features includes: The desensitization rules obtained from the desensitization strategy library that correspond to the sensitivity level label, the field type, and the business intent are determined as the desensitization rules corresponding to the data field; the desensitization strategy library includes sensitivity level labels, field types, business intents, and desensitization rules stored in a mapping relationship.

5. The method according to claim 1, characterized in that, The step of determining the sensitivity level label corresponding to the data field based on the relevant contextual features includes: Determine the rating rules corresponding to the data fields; The sensitivity level label corresponding to the data field is determined based on the relevant contextual characteristics and the rating rules corresponding to the data field.

6. The method according to claim 1, characterized in that, Before performing desensitization processing on the target data corresponding to the data field according to the desensitization rules, the method further includes: Generate a data query statement based on the sensitivity level label and the data field; Execute the data query statement to obtain the target data corresponding to the data field.

7. The method according to claim 1, characterized in that, After obtaining the de-identified data corresponding to the target data, the method further includes: The de-identified data corresponding to the target data is sent to the downstream data requester.

8. A data processing platform, characterized in that, The platform includes: The first processing module is used to determine the relevant contextual features of the data query request; the data query request includes the data field to be queried; the relevant contextual features refer to the features associated with the data query scenario and business requirements of the data query request. The second processing module is used to determine the sensitivity level label corresponding to the data field based on the relevant contextual characteristics. The third processing module is used to determine the desensitization rule corresponding to the data field based on the sensitivity level label corresponding to the data field and the relevant contextual features; The fourth processing module is used to perform desensitization processing on the target data corresponding to the data field according to the desensitization rules, so as to obtain the desensitized data corresponding to the target data.

9. An electronic device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation of the data processing method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The storage medium stores at least one executable instruction, which, when executed on a processor, causes the processor to perform the operation of the data processing method as described in any one of claims 1-7.

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