Power grid application account management system supporting multi-dimensional data desensitization and permission control

Abstract

The application discloses a power grid application account management system supporting multi-dimensional data desensitization and permission control, and particularly relates to the technical field of power grid application account management, and through constructing a multi-dimensional account data feature set, the organization and traceability of account data are improved, through comprehensively analyzing the business sensitive attributes, system correlation and calling frequency of fields, a field comprehensive sensitivity index is constructed and a sensitive level description set is formed, and access subject identity information, role types, task attributes, access terminals and network environments are collected to generate an access subject permission state vector, a multi-dimensional permission matching relationship is established in combination with the sensitive level description set, the accessible range of the access subject under each data dimension is dynamically determined, a hierarchical desensitization strategy is executed on sensitive fields, and the security data view not only meets the access permission requirements, but also guarantees the availability of business data.

Description

Technical Field

[0001] This invention relates to the field of power grid application ledger management technology, and more specifically, to a power grid application ledger management system that supports multi-dimensional data anonymization and access control. Background Technology

[0002] As power grid companies continuously improve their digitalization and informatization levels, a large number of business application systems are deployed in various business scenarios such as production operation, equipment maintenance, and dispatch management. To achieve unified management of various application systems, power grid companies typically use application ledgers to centrally register and maintain the basic information, deployment environment, interface relationships, and operating status of the systems, thereby supporting system maintenance, resource dispatch, and security management.

[0003] In practical applications, power grid application ledgers often involve various types of data, such as system interface addresses, data source paths, business call relationships, and operational configuration parameters. This information not only has high business value but also possesses certain sensitive attributes. Meanwhile, personnel in different positions need to query, maintain, or analyze the ledger data in their work, but due to differences in their responsibilities, their access permissions and data visibility vary significantly.

[0004] However, existing power grid application ledger management systems typically employ a coarse-grained role-based access control approach. This lacks fine-grained control mechanisms at the field or data dimension level during data access. Once a user has access, they can often obtain complete ledger data, which can easily lead to the exposure of sensitive information in a multi-department collaborative access environment. Furthermore, when ledger data is shared between different business systems or transmitted via API calls, the absence of a dynamic data anonymization strategy in conjunction with the access control mechanism can create security risks during the cross-system flow of sensitive data. It may also hinder the effective utilization of business data due to excessive restrictions on data access. Summary of the Invention

[0005] In order to overcome the above-mentioned deficiencies of the prior art, embodiments of the present invention provide a power grid application ledger management system that supports multi-dimensional data anonymization and access control, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A power grid application ledger management system that supports multi-dimensional data anonymization and access control, including a ledger data acquisition module, a data sensitivity labeling module, an access assessment module, an access scope calculation module, a hierarchical anonymization module, and an access audit module;

[0008] The ledger data acquisition module is used to acquire basic information, functional module information, interface call relationship information and operating environment information of the power grid application ledger management system, perform structured parsing and standardization processing on the collected data, and extract multi-dimensional ledger data feature sets.

[0009] The data sensitivity labeling module is used to comprehensively analyze the business sensitivity attributes, system correlation and call frequency of the ledger fields based on the multidimensional ledger data feature set, calculate the comprehensive sensitivity index of the fields, obtain the sensitivity level of each data field and form a ledger data sensitivity level description set.

[0010] The permission assessment module is used to obtain the identity information, role type and access task attributes of the access subject, and construct a set of access behavior context features by combining the access terminal environment and the access source network. It determines the comprehensive permission value of the access subject through permission mapping rules and generates the access subject permission status vector.

[0011] The access scope calculation module is used to establish a permission matching relationship between the access subject's permission status vector and the ledger data sensitivity level description set, and to determine the access scope of the access subject.

[0012] The hierarchical desensitization module is used to perform hierarchical desensitization strategy matching on the ledger data according to the access scope of the access subject, desensitize sensitive fields, and generate a secure data view that meets the access permissions.

[0013] The access audit module is used to return the security data view to the access subject, record access logs, identify abnormal access patterns through access behavior analysis, and output the access security risk level of the ledger data.

[0014] In a preferred embodiment, the process of obtaining the multidimensional ledger data feature set is as follows: deploy a ledger information collection unit and a system operation status collection unit at the application management node of the power grid information platform to obtain the basic information, functional module information, interface call relationship information and operating environment information of the power grid application ledger management system;

[0015] The basic information includes the application name, application identifier, business category, and management unit; the operating environment information includes the deployment node, server resource identifier, and operating platform type; the functional module information includes the module identifier, module name, module type, business category to which the module belongs, module function description, and inter-module dependencies; the interface call relationship information includes the interface identifier, interface name, interface type, interface input and output parameters, number of interface calls, and interface call source.

[0016] A unified data format specification is set for the collected basic information and operating environment information, and semantic recognition and field mapping are performed on the data fields of different source systems through data parsing, so as to convert the same type of information in different systems into a unified field structure;

[0017] Based on the unified field structure, topological parsing is performed on the functional module information and interface call relationship information. By identifying the functional module call path and interface access relationship, an internal module call chain of the application is constructed, and the call chain is associated and mapped with the application deployment node information to obtain application structure description data.

[0018] Based on the application structure description data, feature quantities are extracted from the ledger data, and an application structure feature vector is constructed by statistically analyzing the number of application modules, the number of interface calls, the number of deployment nodes, the length of the propagation path, and the number of associated paths.

[0019] The application structure feature vectors are combined with business categories to construct a multi-dimensional ledger data feature set that describes the structural features and operating environment features of the power grid application ledger management system.

[0020] In a preferred embodiment, the process of obtaining the comprehensive sensitivity index of the field is as follows: performing field-level scanning and semantic recognition on the feature set of multidimensional ledger data, identifying the business meaning of each field through field semantic parsing, and initially labeling the fields with sensitive attributes in conjunction with the power grid information security classification standard;

[0021] The system association relationships in the multidimensional ledger data feature set are structured and parsed. By analyzing the call chain and interface dependency relationship between application modules, the propagation path length and association path number of each field in the cross-module call process are calculated, and the system association strength features of the field in the system structure are extracted.

[0022] Statistical analysis was performed on the frequency of the ledger fields in the historical access logs and interface call records. The number of times each field was called within a unit of time was counted by a sliding time window to obtain the field call frequency characteristics.

[0023] Based on the business sensitivity attribute markers, system association strength characteristics, and field call frequency characteristics, each field is comprehensively evaluated, and the comprehensive sensitivity index of the field is calculated through a weighted fusion method.

[0024] In a preferred embodiment, the sensitive attribute labeling includes device operation information, system interface information, network topology information, and general business information.

[0025] In a preferred embodiment, the process of obtaining the sensitivity level description set of ledger data is as follows: each field is mapped according to the comprehensive sensitivity index of the field, and the ledger fields are divided into high-sensitivity fields, medium-sensitivity fields and low-sensitivity fields by comparing with the preset sensitivity level threshold range, and a corresponding sensitivity level identifier is generated for each field to form a sensitivity level description set of ledger data.

[0026] The rules for classifying the ledger fields by level are as follows:

[0027] If the overall sensitivity index of a field is greater than the threshold of the first sensitivity level, then the field is classified as a highly sensitive field.

[0028] If the overall sensitivity index of a field is greater than or equal to the second sensitivity level threshold and less than or equal to the first sensitivity level threshold, then the field is classified as a medium sensitivity field.

[0029] If the overall sensitivity index of a field is less than the threshold of the second sensitivity level, then the field is classified as a low-sensitivity field.

[0030] In a preferred embodiment, the process of obtaining the comprehensive permission value is as follows: an identity authentication unit and an access behavior collection unit are deployed at the access control node of the power grid application ledger management system to obtain the identity information, role type and access task attributes of the access subject when the access request is initiated.

[0031] After completing the authentication of the access subject, obtain the access terminal environment information corresponding to the access request, and simultaneously obtain the access source network information to form access environment feature data;

[0032] The access subject's identity information, role type, access task attributes, and access environment feature data are uniformly encoded and then structurally combined according to user attribute dimensions, task attribute dimensions, and environment attribute dimensions to construct an access behavior context feature set.

[0033] Based on the pre-established permission mapping rule base, the access behavior context feature set is matched and analyzed. By identifying the relationship between the access subject role type and the access task attribute, the basic permission value of the access subject in the current task scenario is determined.

[0034] After obtaining the basic permission value, the permission level is dynamically adjusted by combining the access terminal environment and the network information of the access source. An environment adjustment coefficient is constructed by the terminal security level and the network security level.

[0035] The comprehensive permission value of the access subject is calculated based on the basic permission value and the environment correction coefficient.

[0036] In a preferred embodiment, the process of determining the accessible scope of the accessing subject is as follows:

[0037] Extract the comprehensive permission value of the access subject from the access subject permission status vector, and simultaneously extract the comprehensive sensitivity index of the accessible fields from the ledger data sensitivity level description set;

[0038] By establishing a permission matching function, the comprehensive permission value is compared and matched with the comprehensive sensitivity index of the field to calculate the access permission level of the access field;

[0039] When an actual access request arrives, the fields requested by the access subject are filtered according to the access permission level of the access fields to determine the secure access range of the access subject in the current task and access environment, including the set of accessible fields and the set of restricted fields.

[0040] In a preferred embodiment, the process of generating a secure data view that satisfies access permissions is as follows:

[0041] For a restricted set of fields, highly sensitive fields will be replaced using a mask.

[0042] For sensitive fields, interval fuzzing is used to map the original value of the field to its corresponding interval range.

[0043] For low-sensitivity fields, a statistical value substitution strategy is adopted to replace the field value with the mean.

[0044] The fields in the restricted field set of each ledger record are processed sequentially, and the fields that can be directly accessed in the accessible field set are retained to generate a secure data record that meets the access subject's permission requirements.

[0045] All security data records are collected to form a security data view.

[0046] In a preferred embodiment, the process of identifying abnormal access patterns through access behavior analysis is as follows:

[0047] The security data view is returned to the access subject through the access interface, along with access response metadata, including the access subject identifier, access time, and set of accessible fields;

[0048] For each access operation, record a detailed access log, including the access subject identifier, access request type, access field list, access timestamp, and access terminal environment information;

[0049] After the access logs are recorded, behavioral feature vectors are extracted by scanning the access logs in real time.

[0050] Based on historical access behavior benchmarks and current behavior feature vectors, an access anomaly index is calculated.

[0051] In a preferred embodiment, the process of determining the security risk level of the output ledger data access is as follows:

[0052] The access anomaly index is compared with a preset risk threshold to classify the access behavior of the access subject into low-risk and high-risk levels, generating a ledger data access security risk level: if the access anomaly index is greater than or equal to the risk threshold, the access behavior of the access subject is classified as high-risk; if the access anomaly index is less than the risk threshold, the access behavior of the access subject is classified as low-risk.

[0053] The technical effects and advantages of this invention are as follows:

[0054] 1. This invention achieves dynamic security management of power grid application ledger data in multi-role, multi-task, and multi-environment access scenarios by organically combining multi-dimensional data feature modeling, sensitivity assessment, refined permission matching, and hierarchical desensitization strategies. It can effectively prevent the leakage of sensitive information and reduce the risk of cross-system data sharing while ensuring data availability. It also provides quantitative basis for access control optimization, risk warning, and security auditing, thereby significantly improving the overall security, management accuracy, and operation and maintenance efficiency of the power grid application ledger management system. Attached Figure Description

[0055] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0056] Figure 1 This is a flowchart of the system according to an embodiment of the present invention. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] Example: The present invention provides, as follows Figure 1 The power grid application ledger management system shown supports multi-dimensional data anonymization and access control, including a ledger data acquisition module, a data sensitivity labeling module, an access assessment module, an access scope calculation module, a hierarchical anonymization module, and an access audit module;

[0059] The ledger data acquisition module is used to acquire basic information, functional module information, interface call relationship information and operating environment information of the power grid application ledger management system. It performs structured parsing and standardization processing on the collected data, constructs a power grid application ledger data structure that includes application identifier, business category, deployment node, data source and interface attributes, and extracts a multi-dimensional ledger data feature set.

[0060] The data sensitivity labeling module is used to comprehensively analyze the business sensitivity attributes, system correlation and call frequency of the ledger fields based on the multidimensional ledger data feature set, calculate the comprehensive sensitivity index of the fields, obtain the sensitivity level of each data field and form a ledger data sensitivity level description set.

[0061] The permission assessment module is used to obtain the identity information, role type and access task attributes of the access subject, and construct a set of access behavior context features by combining the access terminal environment and the access source network. It determines the comprehensive permission value of the access subject through permission mapping rules and generates the access subject permission status vector.

[0062] The access scope calculation module is used to establish a permission matching relationship between the access subject's permission status vector and the ledger data sensitivity level description set, and to determine the access scope of the access subject.

[0063] The hierarchical desensitization module is used to perform hierarchical desensitization strategy matching on the ledger data according to the access scope of the access subject, desensitize sensitive fields, and generate a secure data view that meets the access permissions.

[0064] The access audit module is used to return the secure data view to the access subject and record access logs such as access time, access subject and access field range. It identifies abnormal access patterns through access behavior analysis and outputs the security risk level of the ledger data access.

[0065] In this embodiment of the invention, the process of acquiring basic information, functional module information, interface call relationship information, and operating environment information of the power grid application ledger management system, performing structured parsing and standardization on the collected data, constructing a power grid application ledger data structure including application identifier, business category, deployment node, data source, and interface attributes, and extracting a multi-dimensional ledger data feature set is as follows:

[0066] At the application management node of the power grid information platform, a ledger information collection unit and a system operation status collection unit are deployed to obtain basic information, functional module information, interface call relationship information and operating environment information of the power grid application ledger management system from the application registration center, operation and maintenance management platform and interface gateway;

[0067] The basic information includes the application name, application identifier, business category, and management unit; the operating environment information includes the deployment node, server resource identifier, and operating platform type; the functional module information includes the module identifier, module name, module type, business category to which the module belongs, module function description, and inter-module dependencies; the interface call relationship information includes the interface identifier, interface name, interface type, interface input and output parameters, number of interface calls, and interface call source.

[0068] Preferably, the data acquisition interface verifies the identity with the unified identity management system of the power grid to ensure the credibility and consistency of the data source.

[0069] A unified data format specification is set for the collected basic information and operating environment information. Semantic recognition and field mapping are performed on data fields from different source systems through data parsing. Similar information from different systems is converted into a unified field structure. At the same time, a unique application identifier and timestamp are added to each ledger data, so that the ledger information forms a traceable data record sequence at the data level.

[0070] Based on the unified field structure, topological parsing is performed on the functional module information and interface call relationship information. By identifying the functional module call path and interface access relationship, an internal module call chain of the application is constructed. The call chain is then associated and mapped with the application deployment node information to obtain application structure description data that reflects the relationship between the application structure and the system operating environment.

[0071] Based on the application structure description data, feature quantities are extracted from the ledger data, and an application structure feature vector is constructed by statistically analyzing indicators such as the number of application modules, the number of interface calls, the number of deployment nodes, the length of the propagation path, and the number of associated paths.

[0072] The application structure feature vectors are combined with business categories to construct a multi-dimensional ledger data feature set that describes the structural features and operating environment features of the power grid application ledger management system.

[0073] It should be noted that the multidimensional ledger data feature set not only includes the basic information statistical features of the application system, but also describes the correlation characteristics between the application module structure, interface call relationship and operating environment. Its technical significance lies in providing a structured data foundation for subsequent ledger data sensitivity assessment, permission matching analysis and data desensitization strategy matching, thereby improving the data organization and security control capabilities of the power grid application ledger management system in complex business environments.

[0074] In this embodiment of the invention, the process of comprehensively analyzing the business sensitivity attributes, system relationships, and call frequency of the ledger fields based on the multidimensional ledger data feature set, calculating the comprehensive sensitivity index of the fields, obtaining the sensitivity level of each data field, and forming a ledger data sensitivity level description set is as follows:

[0075] In the power grid application ledger data management platform, the multi-dimensional ledger data feature set that has been constructed is scanned at the field level and semantically recognized. The business meaning of each field is identified through field semantic parsing, and the fields are initially labeled with sensitive attributes in combination with the power grid information security classification standard.

[0076] The sensitive attribute annotations include categories such as equipment operation information, system interface information, network topology information, and general business information, resulting in a set of business sensitive attribute tags for the ledger field;

[0077] Preferably, by constructing a field semantic dictionary and a business rule base, joint matching is performed on field names, field descriptions, and data sources, thereby improving the accuracy of sensitive field identification;

[0078] After completing the labeling of the business-sensitive attributes of the fields, the system association relationships in the multidimensional ledger data feature set are structured and parsed. By analyzing the call chain and interface dependency relationship between application modules, the propagation path length and association path number of each field in the cross-module call process are calculated, and the system association strength features of the fields in the system structure are extracted.

[0079] For example, the first The system association strength of each field can be expressed as: ,in, Indicates the first The number of associated paths involved in the system call chain for each field. Indicates the first The propagation path length of each associated path is used to reflect the scope of the field's propagation influence in the system structure;

[0080] Statistical analysis was performed on the frequency of the ledger fields in the historical access logs and interface call records. The number of times each field was called within a unit of time was counted by a sliding time window to obtain the field call frequency characteristics.

[0081] For example, the first The fields in the time window The frequency of calls within can be expressed as: ,in, Indicates the first The number of times each field is accessed within the time window;

[0082] Based on the business sensitivity attribute markers, system association strength characteristics, and field call frequency characteristics, each field is comprehensively evaluated, and the comprehensive field sensitivity index is calculated through a weighted fusion method.

[0083] For example, the overall sensitivity index of the field It can be represented as: ,in, This indicates the weight of the field's business-sensitive attributes. Indicates the system correlation strength. Indicates the frequency of field access. , , These are preset weight coefficients for the field's business-sensitive attribute weight, system association strength, and field call frequency, respectively. , , All are greater than 0, and satisfy ;

[0084] It should be noted that, , , The settings should be made according to the actual situation. For example, the expert empowerment method can be adopted, which involves inviting experts in relevant fields to determine the preset weight coefficients of each indicator through professional opinion surveys and comprehensive evaluations.

[0085] Based on the comprehensive sensitivity index of the aforementioned fields Each field is mapped hierarchically. By comparing it with the preset sensitivity level threshold range, the ledger fields are divided into high sensitivity fields, medium sensitivity fields and low sensitivity fields. A corresponding sensitivity level identifier is generated for each field to form a sensitivity level description set of ledger data.

[0086] For example, the rules for classifying the levels of ledger fields are as follows:

[0087] If the overall sensitivity index of a field is greater than the threshold of the first sensitivity level, the field is classified as a highly sensitive field, indicating that it involves core business data, key interface information or high-value system resources, and requires strict access control and strong desensitization processing for the access subject.

[0088] If the overall sensitivity index of a field is greater than or equal to the second sensitivity level threshold and less than or equal to the first sensitivity level threshold, then the field is classified as a medium-sensitive field, indicating that it contains important business information or key system relationships, and selective desensitization processing is required within the scope of access permissions.

[0089] If the overall sensitivity index of a field is less than the threshold of the second sensitivity level, the field is classified as a low-sensitivity field, indicating that its information security risk is low and it can be provided directly under general access permissions, or only minimal desensitization processing is required.

[0090] It should be noted that the sensitivity level description set of the ledger data not only reflects the business sensitivity of the field itself, but also considers the propagation impact of the field in the system structure and the actual access frequency. Its technical significance lies in providing a quantitative basis for subsequent multi-dimensional permission matching and data desensitization strategy generation, thereby improving the data security control accuracy of the power grid application ledger management system in complex business environments.

[0091] In this embodiment of the invention, the process of obtaining the access subject's identity information, role type, and access task attributes, constructing an access behavior context feature set by combining the access terminal environment and the access source network, determining the access subject's comprehensive permission value through permission mapping rules, and generating an access subject permission state vector is as follows:

[0092] An identity authentication unit and an access behavior collection unit are deployed at the access control node of the power grid application ledger management system to obtain the identity information, role type and access task attributes of the access subject when an access request is initiated.

[0093] The identity information includes user identifier, department, and account authentication status; the role types include system operation and maintenance roles, business management roles, development and maintenance roles, and audit and supervision roles; and the access task attributes include query tasks, maintenance tasks, and statistical analysis tasks.

[0094] Preferably, the identity authentication unit verifies the identity with the unified identity authentication platform of the power grid to ensure the authenticity and legality of the access subject's identity;

[0095] After completing the authentication of the access subject, the access terminal environment information corresponding to the access request is obtained, including terminal device type, operating system version, terminal security status and access time characteristics. At the same time, the access source network information is obtained, including access IP address, network area identifier and network security level, forming access environment characteristic data.

[0096] The access subject's identity information, role type, access task attributes, and access environment feature data are uniformly encoded and then structurally combined according to user attribute dimensions, task attribute dimensions, and environment attribute dimensions to construct an access behavior context feature set, so that each access request corresponds to a unique access context record.

[0097] Based on the pre-established permission mapping rule base, the access behavior context feature set is matched and analyzed. By identifying the relationship between the access subject role type and the access task attribute, the basic permission value of the access subject in the current task scenario is determined.

[0098] For example, the first The basic permission values ​​for an access subject can be represented as follows: ,in, This indicates the role type code of the accessing entity. Indicates access to task attribute encoding. This represents the function for mapping role, task, and permissions.

[0099] After obtaining the basic permission value, the permission level is dynamically adjusted by combining the access terminal environment and the network information of the access source. An environment adjustment coefficient is constructed by the terminal security level and the network security level.

[0100] For example, the environmental correction factor can be expressed as: ,in, Indicates the terminal security level. Indicates network security level indicators, and These are the preset weighting coefficients for terminal security level and network security level, respectively. , All are greater than 0, and satisfy ;

[0101] It should be noted that, , The settings should be made according to the actual situation. For example, the expert empowerment method can be adopted, which involves inviting experts in relevant fields to determine the preset weight coefficients of each indicator through professional opinion surveys and comprehensive evaluations.

[0102] The comprehensive permission value of the access subject is calculated based on the basic permission value and the environment correction coefficient;

[0103] For example, comprehensive permission values It can be represented as: ;

[0104] The comprehensive permission value is jointly encoded with the access subject's identity identifier to construct an access subject permission state vector: ;

[0105] It should be noted that the access subject permission status vector does not represent a single permission level, but rather a multi-dimensional permission feature that comprehensively reflects the access subject's identity attributes, access task requirements, and access environment security status. Its technical significance lies in providing a dynamic permission determination basis for the subsequent construction of the ledger data access control matrix and the matching of de-identification strategies, thereby improving the accuracy and security of permission control in complex access scenarios of the power grid application ledger management system.

[0106] In this embodiment of the invention, the process of establishing a permission matching relationship between the access subject's permission status vector and the ledger data sensitivity level description set to determine the access scope of the access subject is as follows:

[0107] Extract the comprehensive permission value of the access subject from the access subject permission status vector. Simultaneously, the comprehensive sensitivity index of accessible fields is extracted from the sensitivity level description set of the ledger data. ;

[0108] By establishing a permission matching function, the comprehensive permission values ​​are... Combined sensitivity index with fields Compare and match data to calculate the access permission level of the access fields;

[0109] For example, access permission of the access field It can be represented as: Access permission A value of 1 indicates that access is allowed, and a value of 0 indicates that access is denied;

[0110] When an actual access request arrives, the fields requested by the access subject are filtered according to the access permission level of the access fields to determine the secure access range of the access subject in the current task and access environment, including the set of accessible fields and the set of restricted fields.

[0111] In this embodiment of the invention, the process of performing hierarchical desensitization strategy matching on the ledger data according to the accessibility scope of the accessing subject, desensitizing sensitive fields, and generating a secure data view that meets the access permissions is as follows:

[0112] For a restricted set of fields, highly sensitive fields will be replaced using a mask.

[0113] In an optional embodiment, for example, the field value Convert to partially hidden form: ,in Indicates to retain the previous text The length of a single character;

[0114] For moderately sensitive fields, range fuzzing is used to blur the original values ​​of the fields. Mapped to its corresponding interval range representation;

[0115] In an alternative embodiment, for example, ,in The sensitivity level of the field and the business tolerance are determined to ensure that the data can still support statistical analysis after obfuscation, but without leaking accurate information.

[0116] For low-sensitivity fields, a statistical value substitution strategy is adopted, replacing the field value... Replace with mean: ,in Indicates the number of samples for fields of the same type;

[0117] Based on the aforementioned data anonymization strategy, the fields in the restricted field set of each ledger record are processed sequentially, while the directly accessible fields in the accessible field set are retained, generating secure data records that meet the access subject's permission requirements. ;

[0118] All security data records are collected to form a security data view: It is used for accessing the subject's query, analysis and statistical operations, and at the same time records the fields, strategy type and timestamp information of each de-identification processing operation in the access log;

[0119] It should be noted that the secure data view not only ensures that sensitive field information is precisely controlled under different access permissions, but also maintains the business integrity and analytical availability of the ledger data. Its technical significance lies in achieving dynamic security protection of multi-dimensional fields through a hierarchical desensitization strategy, providing reliable data security for the power grid application ledger management system in multi-role and multi-task access scenarios.

[0120] In this embodiment of the invention, the process of returning the secure data view to the accessing subject and recording access logs such as access time, accessing subject, and access field range, and identifying abnormal access patterns through access behavior analysis and outputting the ledger data access security risk level is as follows:

[0121] The generated security data view The access response is returned to the accessing subject via the access interface, along with access response metadata, including the accessing subject identifier, access time, and set of accessible fields, to ensure that the accessing subject only obtains data content that is permitted by its authority.

[0122] For each access operation, record detailed access logs, including key data such as access subject identifier, access request type, access field list, access timestamp, and access terminal environment information;

[0123] After the access logs are recorded, behavioral feature vectors are extracted by scanning the access logs in real time. ,in This represents the number of visits per unit of time. Indicates the scope of the accessed fields. This indicates the characteristics of the access time distribution. Indicates the characteristics of the access terminal environment;

[0124] Based on historical visit behavior benchmarks With the current behavior feature vector Calculate the access anomaly index : ,in, The index representing the access subject, i.e., the first... This access request, An index representing the characteristics of access behavior. This represents the total number of behavioral feature dimensions, used to comprehensively measure the degree of deviation between the current behavior of the accessing entity and historical benchmarks.

[0125] Access anomaly index The access behavior of the access subject is compared with the preset risk threshold and divided into low-risk and high-risk levels to generate a ledger data access security risk level: if the access anomaly index is greater than or equal to the risk threshold, the access behavior of the access subject is divided into high-risk level; if the access anomaly index is less than the risk threshold, the access behavior of the access subject is divided into low-risk level.

[0126] The access security risk level is associated with the access log and stored to form a complete record of behavioral security.

[0127] It should be noted that the process not only ensures that the secure data view is returned according to access permissions, but also achieves dynamic security assessment of ledger data access through access behavior analysis and anomaly detection. Its technical significance lies in providing real-time risk monitoring capabilities for the power grid application ledger management system and improving the level of ledger data security protection in multi-role and multi-environment access scenarios.

[0128] This invention achieves dynamic security management of power grid application ledger data in multi-role, multi-task, and multi-environment access scenarios by organically combining multi-dimensional data feature modeling, sensitivity assessment, refined permission matching, and hierarchical desensitization strategies. It can effectively prevent the leakage of sensitive information and reduce the risk of cross-system data sharing while ensuring data availability. It also provides quantitative basis for access control optimization, risk warning, and security auditing, thereby significantly improving the overall security, management accuracy, and operation and maintenance efficiency of the power grid application ledger management system.

[0129] This invention constructs a multi-dimensional ledger data feature set to achieve structured analysis and standardized management of the basic information, functional modules, interface call relationships, and operating environment of the power grid application ledger management system. This allows for precise characterization of the data attributes and interaction relationships of each application system, improving the organization and traceability of ledger data. By comprehensively analyzing the business sensitivity attributes, system relationships, and call frequency of fields, a comprehensive field sensitivity index is constructed, forming a sensitivity level description set. This enables the system to quantitatively identify high-risk fields, ensuring scientific hierarchical management of sensitive information under different access scenarios. This is achieved by collecting access subject identity information and role types. Based on task attributes, access terminals, and network environment, an access subject's permission status vector is generated. Combined with a sensitivity level description set, a multi-dimensional permission matching relationship is established, which can dynamically determine the access scope of the access subject under each data dimension. This enables fine-grained permission control based on roles, tasks, and environments. A hierarchical desensitization strategy is implemented for sensitive fields, including mask replacement, interval fuzzification, and statistical value substitution, so that the secure data view can meet access permission requirements while ensuring the availability of business data. Abnormal access patterns are identified through access log recording and behavior analysis, and the security risk level of ledger data access is output, enabling real-time monitoring and risk warning of the access process.

[0130] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0131] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0132] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0133] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion 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 this application. 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.

[0134] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A power grid application ledger management system that supports multi-dimensional data anonymization and access control, characterized by: It includes a ledger data collection module, a data sensitivity labeling module, a permission assessment module, an access scope calculation module, a hierarchical desensitization module, and an access audit module; The ledger data acquisition module is used to collect basic information, functional module information, interface call relationship information and operating environment information of the power grid application ledger management system, and extract multi-dimensional ledger data feature set after structured parsing and standardization of the collected data. The data sensitivity labeling module is used to comprehensively analyze the business sensitivity attributes, system correlations and call frequency of each ledger data field in the multidimensional ledger data feature set, and calculate the comprehensive sensitivity index of each ledger data field to form a ledger data sensitivity level description set. The permission assessment module is used to obtain the identity information, role type and access task attributes of the access subject, and construct a set of access behavior context features by combining the access terminal environment and the access source network. It determines the comprehensive permission value of the access subject through permission mapping rules and generates the access subject permission status vector. The access scope calculation module is used to establish a permission matching relationship between the access subject's permission status vector and the ledger data sensitivity level description set, and to determine the access scope of the access subject. The hierarchical desensitization module is used to perform hierarchical desensitization strategy matching on the ledger data according to the access scope of the access subject, desensitize sensitive fields, and generate a secure data view that meets the access permissions. The access audit module is used to return the security data view to the access subject, record access logs, identify abnormal access patterns through access behavior analysis, and output the access security risk level of the ledger data.

2. The power grid application ledger management system supporting multi-dimensional data anonymization and access control as described in claim 1, characterized in that: The ledger data acquisition module further includes: a ledger information acquisition unit and a system operation status acquisition unit, deployed at the application management node of the power grid information platform; wherein, the ledger information acquisition unit is used to acquire basic information, functional module information, and interface call relationship information of the power grid application ledger management system; the system operation status acquisition unit is used to acquire the operating environment information of the power grid application ledger management system; wherein, the basic information includes application name, application identifier, business category, and affiliated management unit; the operating environment information includes deployment node, server resource identifier, and operating platform type; the functional module information includes module identifier, module name, module type, business category to which the module belongs, module function description, and inter-module dependencies; the interface call relationship information includes interface identifier, interface name, interface type, interface input and output parameters, number of interface calls, and interface call source; therefore, the ledger data acquisition module is specifically used for: A unified data format specification is set for the collected basic information and operating environment information, and semantic recognition and field mapping are performed on the data fields of different source systems through data parsing, so as to convert the same type of information in different systems into a unified field structure; Based on the unified field structure, topological parsing is performed on the functional module information and interface call relationship information. By identifying the functional module call path and interface access relationship, an internal module call chain of the application is constructed, and the call chain is associated and mapped with the application deployment node information to obtain application structure description data. Based on the application structure description data, feature quantities are extracted from the ledger data, and an application structure feature vector is constructed by statistically analyzing the number of application modules, the number of interface calls, the number of deployment nodes, the length of the propagation path, and the number of associated paths. The application structure feature vectors are combined with business categories to construct a multi-dimensional ledger data feature set that describes the structural features and operating environment features of the power grid application ledger management system.

3. The power grid application ledger management system supporting multi-dimensional data anonymization and access control as described in claim 1, characterized in that: The data sensitivity labeling module is specifically used for: The multidimensional ledger data feature set is scanned at the field level and semantically recognized. The business meaning of each field is identified through field semantic parsing, and the fields are initially labeled with sensitive attributes in accordance with the power grid information security classification standard. The system association relationships in the multidimensional ledger data feature set are structured and parsed. By analyzing the call chain and interface dependency relationship between application modules, the propagation path length and association path number of each field in the cross-module call process are calculated, and the system association strength features of the field in the system structure are extracted. Statistical analysis was performed on the frequency of the ledger fields in the historical access logs and interface call records. The number of times each field was called within a unit of time was counted by a sliding time window to obtain the field call frequency characteristics. Based on the business sensitivity attribute markers, system association strength characteristics, and field call frequency characteristics, each field is comprehensively evaluated, and a comprehensive field sensitivity index is calculated through a weighted fusion method to form a set of sensitivity level descriptions for ledger data.

4. The power grid application ledger management system supporting multi-dimensional data anonymization and access control as described in claim 3, characterized in that: The sensitive attribute annotations include device operation information, system interface information, network topology information, and general business information.

5. The power grid application ledger management system supporting multi-dimensional data anonymization and access control as described in claim 3, characterized in that: The process of forming a sensitivity level description set for ledger data is as follows: each field is mapped to a grade according to the comprehensive sensitivity index of the field, and the ledger field is divided into high-sensitivity fields, medium-sensitivity fields and low-sensitivity fields by comparing with the preset sensitivity level threshold range. A corresponding sensitivity level identifier is generated for each field to form a sensitivity level description set for ledger data. The rules for classifying the levels of the ledger fields are as follows: If the overall sensitivity index of a field is greater than the threshold of the first sensitivity level, then the field is classified as a highly sensitive field. If the overall sensitivity index of a field is greater than or equal to the second sensitivity level threshold and less than or equal to the first sensitivity level threshold, then the field is classified as a medium sensitivity field. If the overall sensitivity index of a field is less than the threshold of the second sensitivity level, then the field is classified as a low-sensitivity field.

6. The power grid application ledger management system supporting multi-dimensional data anonymization and access control as described in claim 1, characterized in that: The permission assessment module further includes an access behavior collection unit and an identity authentication unit, deployed at the access control node of the power grid application ledger management system; wherein, the access behavior collection unit is used to obtain the identity information, role type and access task attributes of the access subject when an access request is initiated; the identity authentication unit is used to authenticate the access subject. The permission evaluation module is specifically used for: After completing the authentication of the access subject, obtain the access terminal environment information corresponding to the access request, and simultaneously obtain the access source network information to form access environment feature data; The access subject's identity information, role type, access task attributes, and access environment feature data are uniformly encoded and then structurally combined according to user attribute dimensions, task attribute dimensions, and environment attribute dimensions to construct an access behavior context feature set. Based on the pre-established permission mapping rule base, the access behavior context feature set is matched and analyzed. By identifying the relationship between the access subject role type and the access task attribute, the basic permission value of the access subject in the current task scenario is determined. After obtaining the basic permission value, the permission level is dynamically adjusted by combining the access terminal environment and the network information of the access source. An environment adjustment coefficient is constructed by the terminal security level and the network security level. The comprehensive permission value of the access subject is calculated based on the basic permission value and the environment correction coefficient.

7. The power grid application ledger management system supporting multi-dimensional data anonymization and access control as described in claim 1, characterized in that: The access range calculation module is specifically used for: Extract the comprehensive permission value of the access subject from the access subject permission status vector, and simultaneously extract the comprehensive sensitivity index of the accessible fields from the ledger data sensitivity level description set; By establishing a permission matching function, the comprehensive permission value is compared and matched with the comprehensive sensitivity index of the field to calculate the access permission level of the access field; When an actual access request arrives, the fields requested by the access subject are filtered according to the access permission level of the access fields to determine the safe access range of the access subject in the current task and access environment. The safe access range includes a set of accessible fields and a set of restricted fields.

8. The power grid application ledger management system supporting multi-dimensional data anonymization and access control according to claim 7, characterized in that: The hierarchical desensitization module is specifically used for: For a restricted set of fields, highly sensitive fields will be replaced using a mask. For sensitive fields, interval fuzzing is used to map the original value of the field to its corresponding interval range. For low-sensitivity fields, a statistical value substitution strategy is adopted to replace the field value with the mean. The fields in the restricted field set of each ledger record are processed sequentially, and the fields that can be directly accessed in the accessible field set are retained to generate a secure data record that meets the access subject's permission requirements. All security data records are collected to form a security data view.

9. The power grid application ledger management system supporting multi-dimensional data anonymization and access control as described in claim 8, characterized in that: The process of returning the security data view to the accessing subject and recording access logs, and identifying abnormal access patterns through access behavior analysis, specifically includes: The security data view is returned to the access subject through the access interface, along with access response metadata, including the access subject identifier, access time, and set of accessible fields; For each access operation, record a detailed access log, including the access subject identifier, access request type, access field list, access timestamp, and access terminal environment information; After the access logs are recorded, behavioral feature vectors are extracted by scanning the access logs in real time. Based on historical access behavior benchmarks and current behavior feature vectors, an access anomaly index is calculated.

10. The power grid application ledger management system supporting multi-dimensional data anonymization and access control according to claim 9, characterized in that: The security risk level for accessing the output ledger data is as follows: The access anomaly index is compared with the preset risk threshold to classify the access behavior of the access subject into low-risk and high-risk levels, and generate the access security risk level of the ledger data: if the access anomaly index is greater than or equal to the risk threshold, the access behavior of the access subject is classified into the high-risk level. If the access anomaly index is less than the risk threshold, the access behavior of the accessing subject will be classified as low risk.

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