Method and system for calculating privacy value of power enterprise data

Abstract

The invention relates to the technical field of network security in the power generation industry, and discloses a calculation method and system for a data privacy value of a power enterprise. Comprising the steps that data classification is performed according to an energy industry data classification guide, data classification and information system permutation and combination are performed to obtain a data protection directory, and classification data values are calculated according to system network security level protection levels and quantity; extracting information such as post levels of sub-class data producers and consumers, the number of circulation nodes, data quality and the like from an information system according to the data catalog; for enterprise-level data, constructing a producer and consumer unary array, and calculating a data circulation range, a subclass data use value and a privacy value by adopting a matrix method; for personal level data, a privacy value is calculated based on a range of data flow and a number of user accesses. The data value and the privacy value are calculated according to the data circulation characteristics of the power enterprise and the importance of an information system, a calculation method is provided from the two protection subject angles of individuals and enterprises, and reference and basis are provided for the power enterprise to make a data security protection strategy.

Description

Technical Field

[0001] The present invention relates to the field of network security technology in the power generation industry, and specifically to a method and system for calculating the privacy value of data in a power enterprise. Background Art

[0002] Amidst the booming digital economy, the implementation of a series of laws and regulations, including the Cybersecurity Law and the Data Security Law, has not only clarified the importance of data as a core strategic resource for digital development but also elevated it to the forefront of the power industry's production. This has led to unprecedented attention on the issue of data security. As a key national information infrastructure, the energy and power sector faces new challenges, including rising data breach risks and increased security protection difficulties, driven by factors such as the accelerated interconnection of power systems, increasingly sophisticated hacker attack methods, and the reliance on intelligent, networked devices for data storage. These challenges not only expose core data such as grid operation data and user electricity usage to the risk of illegal theft, but also threaten the integrity and availability of sensitive information such as power trading data and equipment status monitoring data. To address the potential for user trust crises, economic losses, and even national security risks posed by privacy data leaks, power companies urgently need to develop a systematic privacy data identification system, utilizing advanced algorithmic models and assessment mechanisms to accurately quantify the degree of data privacy. Only based on accurate privacy assessment results can we deploy technical measures such as data encryption, access control, and security auditing in a targeted manner, and ultimately achieve dynamic and intelligent security management of privacy data throughout its entire life cycle, from generation, collection, transmission, storage to use and destruction, and build a solid line of defense for data security in the power industry.

[0003] Identifying data privacy and calculating the degree of privacy are key to ensuring data security for power companies. However, power company data involves production data, operational data, security data, financial data, personal data, and more. Data is distributed across various information systems, and network security partitions involve management information areas and production control areas. It involves both time-series and relational data, which are complex, interconnected, and growing in volume. Identifying data privacy and calculating privacy values ​​pose significant challenges. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a method and system for calculating the data privacy value of electric power enterprises. The method and system have the advantages of giving the value of classified data based on the number and importance of information systems that the classified data depends on, extracting characteristic parameters such as the use value and circulation scope of the data according to the business process of the data in the information system, and providing a method for calculating the data privacy value from the protection subjects at both the enterprise and individual levels, which can meet the data security needs of electric power enterprises.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for calculating the privacy value of power enterprise data, comprising the following steps: Step 1: Classify data according to the energy industry data classification guidelines, arrange and combine data classification and information systems to obtain a data protection catalog, and calculate the value of classified data based on the system network security level protection level and quantity; Step 2: Extract the sub-category data producer and consumer job level, circulation node number, and data quality from the information system according to the data protection catalog; Step 3: For enterprise-level data, construct a one-element array of producers and a one-element array of consumers, calculate the data circulation range, sub-category data usage value, and sub-category data privacy value; for personal-level data, calculate the personal privacy value based on the data circulation range and the number of user visits.

[0006] Preferably, in step one, the data classification is specifically divided into 17 categories, including scientific and technological management data, planning and construction data, production monitoring data, industrial control data, industrial production status information, production process parameter information, log information, network and system operation and maintenance data, network security management data, maintenance management data, enterprise operation and management data, network and system management resource data, equipment asset management resource data, personal information data, exchange data, sharing data, and transaction data.

[0007] Preferably, in step 1, the data protection directory is formed by associating the 17 categories of data classification with the information system, eliminating the classifications that have no corresponding entries in the system, and forming the data protection directory.

[0008] Preferably, in step 1, the classification data value The calculation formula is:

[0009] In the calculation formula, Represents the classification data value, Represents the number of different level systems involved in the data, Representative The number of systems, Representative The weight of a system.

[0010] Preferably, in step 3, the expression of the data producer unary array is:

[0011] In the expression, Represents a one-element array of data producers, Representing the producer level, The number of representative producers, Represents producer data quality; The expression of the data consumer unary array is:

[0012] In the expression, Represents a one-element array of data consumers, Representing the consumer level, The number of representative consumers, Represents the frequency of consumer data access.

[0013] Preferably, in step 3, the data circulation range Producer one-element array, consumer one-element array, and number of nodes not passed in the key business chain based on subclass data , using matrix multiplication calculation, the subclass data usage value Based on the value of classified data and the number of key business chains Calculate the privacy value of the subclass data Based on the scope of data circulation and use value Perform calculations; Scope of data circulation The calculation formula is:

[0014] The sub-category data usage value The calculation formula is:

[0015] The subclass data privacy value The calculation formula is:

[0016] Preferably, in step 4, the personal privacy value Based on the value of classified data Number of nodes that are not passed in the key business chain information flow , Number of system user visits , Number of visits by individual users Calculated, the personal privacy value The calculation formula is:

[0017] Preferably, during the calculation of the privacy value of the sub-category data, the data privacy value is calculated based on the use value of the sub-category data and the business process of the data in the system. For high-privacy value data, the specific links for implementing security protection measures can be accurately located.

[0018] Preferably, the use value of the sub-category data in the data protection directory in the information system may be 0.

[0019] A system for calculating the privacy value of power enterprise data, applied to a method for calculating the privacy value of power enterprise data, includes a data classification and catalog management module, a data extraction module, and a data calculation module; The data classification and catalog management module is used to classify data according to the energy industry data classification guidelines, and to obtain a data protection catalog by arranging and combining data classification and information systems, and then calculating the value of the classified data; The data extraction module extracts sub-category data producers and consumers data from the information system according to the data protection catalog; For enterprise-level data, the data calculation module constructs a producer unary array and a consumer unary array, calculates the data circulation range, sub-category data usage value, and sub-category data privacy value; for personal-level data, calculates the personal privacy value based on the data circulation range and the number of user visits.

[0020] Compared with the existing technology, the present invention provides a method and system for calculating the privacy value of power enterprise data, which has the following beneficial effects: The present invention is implemented through the following refined process: First, strictly follow the energy industry data classification guidelines, subdivide the data into 17 categories: scientific and technological management data, planning and construction data, production monitoring data, industrial control data, industrial production status information, production process parameter information, log information, network and system operation and maintenance data, network security management data, maintenance management data, enterprise management data, network and system management resource data, equipment asset management resource data, personal information data, exchange data, sharing data, and transaction data, and map and combine them with the information system, eliminate entries without actual mapping, and form a structured data protection directory. Secondly, based on the sub-category data in the directory, extract producer and consumer user information from the information system management module. The job level, number and number of circulation nodes in the key business chain are counted, and the data quality level is obtained in combination with the data security assessment report. The access frequency is extracted from the system operation log. Then, the producer unary array and consumer unary array are constructed for the enterprise-level data. The data circulation range is calculated by weighting the job level. The use value is evaluated by combining the data quality and access frequency, and the privacy value of the sub-category data is further quantified. For personal-level data, the personal privacy value is calculated based on the product of the data circulation range and the number of accesses, combined with the job sensitivity adjustment coefficient. This method realizes the accurate quantification of data value and risk through classification mapping, feature extraction, weighted calculation and dynamic adjustment, providing a scientific basis for the targeted deployment of protection measures, optimization of resource allocation and meeting compliance requirements.

[0021] The present invention gives the value of classified data based on the number and importance of information systems that the classified data depends on, extracts characteristic parameters such as the use value and circulation scope of the data according to the business process of the data in the information system, and provides a calculation method for the data privacy value from the perspective of protection subjects at both the enterprise and individual levels, which can meet the data security needs of power companies. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A diagram showing the steps of a method for calculating the privacy value of power enterprise data in an embodiment of the present invention; Figure 2 Schematic diagram of the calculation system flow of the power enterprise data privacy value in an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0024] See also Figure 1 A method for calculating the privacy value of power enterprise data includes the following steps: Step 1: Obtain data classification according to the energy industry data classification guide, including 17 categories of data: scientific and technological management data, planning and construction data, production monitoring data, industrial control data, industrial production status information, production process parameter information, log information, network and system operation and maintenance data, network security management data, maintenance management data, enterprise operation and management data, network and system management resource data, equipment asset management resource data, personal information data, exchange data, sharing data, and transaction data. The data classification and information system are arranged and combined, and entries with empty mappings of data classifications in the information system are eliminated to obtain a data protection directory. According to the protection levels in the cybersecurity level protection assessment report, the number of systems with different cybersecurity protection levels corresponding to the 17 types of data is counted, and the classified data value of the 17 types of data is calculated through weighted summation; Classification data value The calculation formula is:

[0025] In the calculation formula, Represents the classification data value, Represents the number of different level systems involved in the data, Representative The number of systems, Representative The weight of each system; By strictly following the energy industry data classification guidelines, power enterprise data is divided into 17 categories: scientific and technological management data, planning and construction data, production monitoring data, industrial control data, industrial production status information, production process parameter information, log information, network and system operation and maintenance data, network security management data, maintenance management data, enterprise operation and management data, network and system management resource data, equipment asset management resource data, personal information data, exchange data, sharing data, and transaction data. These 17 categories of data are mapped and combined with the information system, and entries without actual mapping are eliminated to form a structured data protection directory, achieving the following: Standardization and normalization: Unify data classification standards, clarify the definition and boundaries of each type of data, avoid classification ambiguity or duplication, and provide a consistent basis for subsequent analysis; Accurately locate data assets: By mapping the association between classified data and information systems, the storage location and circulation path of each type of data in the actual system can be clarified, invalid entries can be eliminated, and the redundancy of subsequent calculations can be reduced; Build a privacy computing foundation: The generated data protection catalog provides structured input for steps 2 to 4, ensuring the coherence and operability of the privacy value calculation logic; Improve data management efficiency and security: Based on the classification catalog, key data categories can be quickly identified and protective measures can be deployed in a prioritized manner, while meeting energy industry compliance requirements and reducing data leakage risks. Step 1 builds a clear data asset map for power companies through standardized classification, precise mapping, and invalid data filtering. This serves as both the starting point for privacy value calculation and the core framework for data security management. Step 2: Based on the subcategory data in the data protection catalog, export all users of subcategory data producers and consumers from the information system management module, count the user job levels and quantity, with a higher job level indicating a higher level. Based on the information flow of the key business chain of the information system, sort out the number of nodes that the subcategory data passes through and the number of nodes that it does not pass through. Determine the data quality based on the data security assessment report, and extract and count the access frequency of each subcategory data from the information system operation log; Based on the sub-category data of the data protection catalog, key information such as producers, consumers, and circulation nodes is extracted from the information system, and job level, access frequency, and data quality are counted to achieve: Accurately locate data responsible parties: By deriving the producers and consumers of sub-category data, the responsibility boundaries of data flow are clarified, providing a basis for subsequent calculation of circulation scope and privacy value; Quantifying data circulation characteristics: Sorting out the number of nodes and non-passing nodes of sub-category data in key business chains, combined with access frequency statistics, reveals the actual circulation path of data and exposure risks; Assess data quality and risk: Based on the data security assessment report, factor data quality into the calculation to ensure that the privacy value reflects the true value and potential threats of the data; Supporting differentiated protection strategies: Through job level and circulation node analysis, highly sensitive links are identified, providing data support for the formulation of targeted protection measures; Step 3: For enterprise-level data, construct a one-element array of producers and a one-element array of consumers, and calculate the data circulation scope, sub-category data usage value, and sub-category data privacy value; The expression of the data producer unary array is:

[0026] In the expression, Represents a one-element array of data producers, Representing the producer level, The number of representative producers, Represents the producer data quality, which is divided into high, medium, and low, with values ​​of 3, 2, and 1; The expression of the one-element array of data consumers is:

[0027] In the expression, Represents a one-element array of data consumers, Representing the consumer level, The number of representative consumers, represents the frequency of consumer data access; Data circulation scope Producer one-element array, consumer one-element array, and number of nodes not passed in the key business chain based on subclass data , using matrix multiplication calculation, subclass data usage value Based on the value of classified data and the number of key business chains Calculate the privacy value of subclass data Based on the scope of data circulation and use value Perform calculations; Scope of data circulation The calculation formula is:

[0028] Subcategory data usage value The calculation formula is:

[0029] Subclass data privacy value The calculation formula is:

[0030] By constructing a one-element array of producers and a one-element array of consumers, the data circulation scope, usage value and privacy value are calculated. Based on the job level and number of producers and consumers, combined with the circulation nodes of data in the business chain, the circulation scope of sub-category data is accurately calculated to avoid subjective assessment bias; through the correlation analysis between circulation scope and usage value, highly sensitive data is identified to provide a priority basis for privacy protection; data value and privacy risks are quantified to help enterprises balance data utilization and security protection; through structured calculations, complex data relationships are converted into actionable indicators, reducing manual analysis costs and enhancing the scientific nature of privacy management; accurate quantification of data value and privacy risks is achieved, providing an objective basis for enterprises to formulate differentiated protection strategies and optimize data resource allocation, while improving privacy management efficiency and compliance; For personal-level data, the personal privacy value is calculated based on the data circulation range and user access times; Personal privacy value Based on the value of classified data Number of nodes that are not passed in the key business chain information flow , Number of system user visits , Number of visits by individual users Calculated, personal privacy value The calculation formula is:

[0031] Calculate personal privacy values ​​based on the scope of data circulation and the number of user accesses. For personal information data, accurately assess the potential risk of personal privacy leakage by quantifying the scope of circulation and access frequency; dynamically adjust the privacy value weight based on the user's job level and access behavior to avoid underestimation or overestimation caused by static rules; identify highly sensitive personal data through privacy value sorting, and prioritize encryption, desensitization and other measures to achieve precise resource allocation; quantify personal privacy risks while enhancing user trust in corporate data management; transform personal privacy risks from abstract concepts into calculable and comparable values, providing a scientific basis for enterprises to formulate targeted protection strategies and optimize resource allocation, while reducing compliance risks and enhancing user trust; During the calculation of the privacy value of sub-category data, the data privacy value is calculated based on the usage value of the sub-category data and the business process of the data in the system. For high-privacy value data, the specific links for implementing security protection measures can be accurately located; By quantifying the flow path and usage value of data in business processes, we can accurately identify high-risk links for privacy leakage, thereby deploying targeted encryption, auditing and other measures to avoid blind protection. At the same time, combining compliance requirements with business needs, we prioritize the protection of key nodes of high-value data, reducing the risk of leakage while ensuring the efficient operation of the business chain, achieving a dynamic balance between security and efficiency. The usage value of sub-category data in the information system in the data protection catalogue may be zero. In the data privacy value calculation method for power enterprises, the use value of subcategory data in the data protection catalog reflects its actual utility and importance in the business. However, the use value of some subcategory data of 0 indicates that such data exists but has no actual business significance or has been abandoned by the system. By identifying data with a use value of 0, invalid interference items can be quickly eliminated, focusing on core business data such as production monitoring and industrial control, thereby improving the accuracy and efficiency of privacy value calculation. For data with a use value of 0, the protection priority can be lowered or directly cleaned up to avoid wasting resources such as encryption and auditing, while also reducing privacy risks caused by misoperation of such data. Combining use value and privacy value, high-value and high-privacy data (such as data) can be distinguished from low-value and low-privacy data, promoting enterprises to formulate differentiated strategies. By quantifying use value, privacy protection measures can be ensured to meet actual business needs, avoiding excessive protection that affects system performance, and meeting regulatory requirements for data classification and grading management. By filtering invalid data and focusing on high-value targets, the scientific nature of privacy value calculation is improved, and a basis is provided for enterprises to optimize data management and reduce security costs, ultimately achieving a dynamic balance between data value and risk. A system for calculating the privacy value of power enterprise data, applied to a method for calculating the privacy value of power enterprise data, includes a data classification and catalog management module, a data extraction module, and a data calculation module; The data classification and catalog management module is used to classify data according to the energy industry data classification guidelines, and to obtain a data protection catalog by arranging and combining data classification and information systems, and then calculate the value of the classified data. Specifically, this module strictly follows the energy industry data classification standards, divides power enterprise data into 17 categories, such as scientific and technological management, planning and construction, and production monitoring, and removes entries without actual mapping through mapping and combination with information systems to form a structured data protection catalog. At the same time, according to the protection level in the system network security level protection assessment and grading report, the number of systems and weights corresponding to each type of data are counted, and the value of the classified data is calculated through weighted summation, providing a quantitative basis for subsequent analysis; The data extraction module extracts sub-category data producers and consumers from the information system according to the data protection catalog. Specifically, based on the sub-category data in the data protection catalog, this module exports all user information of producers and consumers from the information system management module, including job level and quantity, and the number of circulation nodes in the key business chain. At the same time, it combines data security assessment reports to determine data quality and extracts access frequency of sub-category data from system operation logs to provide detailed input for calculating data circulation scope and usage value. For enterprise-level data, the data calculation module constructs a one-element array of producers and a one-element array of consumers, and calculates the data circulation range, the use value of sub-category data, and the privacy value of sub-category data. For personal-level data, it calculates the personal privacy value based on the data circulation range and the number of user visits. Specifically, this module calculates the circulation range and use value of enterprise-level data by combining the job level and number of producers and consumers with the circulation nodes of data in the business chain. At the same time, it quantifies the privacy value of sub-category data based on data quality, access frequency, and circulation path. For personal-level data, it generates a personal privacy value through weighted calculation of the data circulation range and the number of visits, providing a basis for precise protection.

[0032] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for calculating the privacy value of power enterprise data, characterized in that: The following steps are involved: Classify data according to the energy industry data classification guidelines, arrange and combine data classification and information systems to obtain a data protection catalog, and calculate the value of classified data based on the system network security level protection level and quantity; Extract sub-category data from information systems based on the data protection catalog, including producer and consumer job levels, number of circulation nodes, and data quality; For enterprise-level data, a one-element array of producers and a one-element array of consumers are constructed to calculate the data circulation scope, sub-category data usage value, and sub-category data privacy value; for personal-level data, the personal privacy value is calculated based on the data circulation scope and the number of user visits.

2. The method for calculating the privacy value of power enterprise data according to claim 1, characterized in that: The data classification is specifically divided into 17 categories, including scientific and technological management data, planning and construction data, production monitoring data, industrial control data, industrial production status information, production process parameter information, log information, network and system operation and maintenance data, network security management data, maintenance management data, enterprise operation and management data, network and system management resource data, equipment asset management resource data, personal information data, exchange data, sharing data, and transaction data.

3. The method for calculating the privacy value of power enterprise data according to claim 2, characterized in that: The data protection directory is formed by associating 17 categories of data classification with the information system, eliminating categories that have no corresponding entries in the system, and forming a data protection directory.

4. The method for calculating the privacy value of power enterprise data according to claim 3, characterized in that: The classification data value The calculation formula is: In the calculation formula, Represents the classification data value, Represents the number of different level systems involved in the data, Representative The number of systems, Representative The weight of a system.

5. The method for calculating the privacy value of power enterprise data according to claim 4, characterized in that: The expression of the data producer unary array is: In the expression, Represents a one-element array of data producers, Representing the producer level, The number of representative producers, Represents producer data quality; The expression of the data consumer unary array is: In the expression, Represents a one-element array of data consumers, Representing the consumer level, The number of representative consumers, Represents the frequency of consumer data access.

6. The method for calculating the privacy value of power enterprise data according to claim 5, characterized in that: Scope of data circulation Producer one-element array, consumer one-element array, and number of nodes not passed in the key business chain based on subclass data , using matrix multiplication calculation, the subclass data usage value Based on the value of classified data and the number of key business chains Calculate the privacy value of the subclass data Based on the scope of data circulation and use value Perform calculations; Scope of data circulation The calculation formula is: The sub-category data usage value The calculation formula is: The subclass data privacy value The calculation formula is: 。 7. The method for calculating the privacy value of power enterprise data according to claim 6, characterized in that: The personal privacy value Based on the value of classified data Number of nodes that are not passed in the key business chain information flow , Number of system user visits , Number of visits by individual users Calculated, the personal privacy value The calculation formula is: 。 8. The method for calculating the privacy value of power enterprise data according to claim 7, characterized in that: During the calculation of the privacy value of the sub-category data, the data privacy value is calculated based on the usage value of the sub-category data and the business process of the data in the system. For high-privacy value data, the specific links for implementing security protection measures can be accurately located.

9. The method for calculating the privacy value of power enterprise data according to claim 8, characterized in that: The usage value of the sub-category data in the data protection directory in the information system is 0.

10. A system for calculating the privacy value of electric power enterprise data, based on the method for calculating the privacy value of electric power enterprise data according to any of claims 1 to 9, characterized in that: It includes data classification and catalog management module, data extraction module and data calculation module; The data classification and catalog management module is used to classify data according to the energy industry data classification guidelines, and to obtain a data protection catalog by arranging and combining data classification and information systems, and then calculating the value of the classified data; The data extraction module extracts sub-category data producers and consumers data from the information system according to the data protection catalog; For enterprise-level data, the data calculation module constructs a producer unary array and a consumer unary array, calculates the data circulation range, sub-category data usage value, and sub-category data privacy value; for personal-level data, calculates the personal privacy value based on the data circulation range and the number of user visits.

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