Shared data hiding method and system for distribution automation system

Abstract

The invention discloses a shared data hiding method and system for a distribution automation system. The method comprises the following steps: acquiring cloud master station data of the power distribution automation system, grading and classifying the cloud master station data of the power distribution automation system, and dividing a sensitive data range; differentiated full-life-cycle management is carried out on data of different levels and classes based on the grading and classification results, wherein the differentiated full-life-cycle management comprises data collection, data transmission, data storage, data use, data exchange and data destruction; and performing association risk analysis, data behavior analysis and comprehensive analysis on the data according to the data managed in the full life cycle, and performing shared data hiding and data leakage tracing based on an analysis result. According to the scheme, confidentiality and traceability of the key distribution network service data in the sharing and transmission process are guaranteed, and the security protection capability of the shared data of the cloud master station of the distribution automation system is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power data security, and in particular relates to a method and system for hiding shared data in a distribution automation system. Background Art

[0002] The circulation and processing of data between different organizations has become an inevitable trend. As an important asset of organizations, data faces data security risks in traditional environments on the one hand, and data security risks unique to big data environments on the other. Data security has become a common concern for various organizations in the current industrial environment.

[0003] At present, the power monitoring system has established a relatively complete network security protection system in terms of secure network communication, secure computing environment, and security management center, including network security management organization and system processes, technical system and operation and maintenance guarantee system, forming a security protection capability for data carriers such as terminals, hosts, networks, and applications. However, at the data security protection level, there is still a lack of single-point data security protection construction such as data classification and grading, access control data encryption, data auditing, and data desensitization. The data security protection for the data itself and the network security protection system for data carriers have not yet formed an organic integration, and there is a lack of overall protection management capabilities around the data life cycle flow process. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the present invention provides a method and system for hiding shared data in a distribution automation system, so as to solve the technical problem of improving the security protection capability of shared data of a cloud master station of a distribution automation system.

[0005] In order to solve the above technical problems, the present invention adopts the following technical solutions.

[0006] The present invention first discloses a method for hiding shared data in a power distribution automation system, the method comprising the following steps:

[0007] Obtaining distribution automation system cloud master station data, grading and classifying the distribution automation system cloud master station data, and dividing the sensitive data range;

[0008] Based on the classification and grading results, differentiated full life cycle management is carried out for data of different levels and categories, including data collection, data transmission, data storage, data use, data exchange and data destruction;

[0009] Based on the data managed throughout the entire life cycle, correlation risk analysis, data behavior analysis and comprehensive analysis are performed on the data, and shared data anonymity and data leakage tracing are performed based on the analysis results.

[0010] The present invention further includes the following preferred embodiments:

[0011] The grading of the distribution automation system cloud master station data includes dividing the data into commercial confidential data, important data, general data and public data; the classification of the distribution automation system cloud master station data includes dividing the data into model data and operation data.

[0012] The performing of correlation risk analysis on the data further includes:

[0013] Combined with the big data analysis engine, it conducts real-time analysis of massive data within the distribution network cloud master station and across systems, and monitors whether the business data contains abnormal risks;

[0014] The correlation between business data in the distribution network cloud master station and the inherent connection between data are used for analysis to discover the associated risks of massive discrete data in the distribution network, so as to associate the hidden risks between different businesses in the distribution network.

[0015] The data anonymization includes dynamic anonymization and static anonymization. The dynamic anonymization is applied to the scenario of real-time access to business data, and the data is differentiated and anonymized based on whitelist and SQL rewriting technology; the static anonymization is applied to development testing, data analysis, and external third-party data sharing scenarios, and batch data anonymization is achieved according to the set anonymization rules;

[0016] The method of hiding shared data based on the analysis result further includes:

[0017] Combined with the sensitive data identification of the distribution automation system, in the process of sensitive information identification and sensitive algorithm selection, machine learning methods and data desensitization algorithms are used to evaluate the desensitization results from the dimensions of sensitive information removal degree, data loss, and computing overhead, to form a sensitive data desensitization solution suitable for the distribution automation system.

[0018] The present invention also discloses a system for hiding shared data in a distribution automation system using the aforementioned method for hiding shared data in a distribution automation system, comprising:

[0019] A grading and classification module is used to obtain the distribution automation system cloud master station data, grade and classify the distribution automation system cloud master station data, and divide the sensitive data range;

[0020] The full life cycle management module is used to perform differentiated full life cycle management of data of different levels and categories based on the classification and classification results, including data collection, data transmission, data storage, data use, data exchange and data destruction;

[0021] The data analysis module is used to perform correlation risk analysis, data behavior analysis and comprehensive analysis on the data managed throughout the entire life cycle, and to anonymize shared data and trace data leakage based on the analysis results.

[0022] Accordingly, the present application also discloses a terminal, including a processor and a storage medium;

[0023] The storage medium is used to store instructions;

[0024] The processor is used to operate according to the instructions to execute the steps of the aforementioned method for hiding shared data in a distribution automation system.

[0025] Correspondingly, the present application also discloses a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the aforementioned method for hiding shared data in a distribution automation system are implemented.

[0026] The beneficial effect of the present invention is that, compared with the prior art, the present invention provides a method and system for hiding shared data in a distribution automation system, which ensures the confidentiality and traceability of key distribution network business data during sharing and transmission, realizes situational awareness of data threats, and enhances the security protection capability of shared data of the distribution automation system cloud master station. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a diagram of the cloud master station shared data security protection architecture in the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the present invention more clear, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0029] The embodiments described in this application are only some embodiments of the present invention, not all embodiments. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the protection scope of the present invention.

[0030] In view of the shortcomings of the prior art, the present invention proposes a method and system for hiding shared data in a distribution automation system. Based on massive data risk assessment, data hiding, watermarking and traceability technology, the storage, transmission, interaction and other life cycles of the cloud master station shared data are managed and controlled, and data relevance, operation behavior, data value and other aspects are analyzed and counted to ensure the confidentiality and traceability of key distribution network business data during sharing and transmission, realize situational awareness of data threats, and enhance the security protection capability of the cloud master station shared data of the distribution automation system. The overall data security protection architecture is as follows: Figure 1 shown.

[0031] The method for hiding shared data in a power distribution automation system disclosed in the present invention comprises the following steps:

[0032] Step 1: Obtain the distribution automation system cloud master station data, classify and categorize the distribution automation system cloud master station data, and divide the sensitive data range.

[0033] The data of the cloud master station of the distribution automation system has the characteristics of multi-system and cross-business flow, sharing and collaboration. The traditional data storage and access method based on a single local system is difficult to ensure the reliability and performance of the data under the virtual cloud master station architecture. The present invention comprehensively considers the consistency of the data subject and arranges it systematically according to its internal connection to ensure that the category is unique, the structure is reasonable, the hierarchy is clear, redundancy is reduced, and stable factors are used as the basis for classification to improve the scalability, compatibility and stability of the classification system.

[0034] By classifying and categorizing shared data, we formulate data protection levels applicable to different levels, and determine the subsequent sensitive data network security risk assessment of the distribution network cloud master station based on the classification results. By classifying and grading data, we clarify the categories and levels of the data, and provide support for differentiated and refined security protection in all aspects of the subsequent data life cycle. By sorting out the existing data assets of the distribution automation system cloud master station, we formulate data classification and grading principles applicable to the distribution network cloud master station, and provide a basis for data security protection in data transmission, storage, processing, exchange, destruction and other links.

[0035] In one embodiment, the data classification method of the distribution automation system is shown in Table 1:

[0036]

[0037]

[0038] Table 1

[0039] The data classification method of the distribution automation system includes dividing the data into model data and operation data.

[0040] The model data is the public, parameter, and relationship information abstracted from the control object entity, which is used to construct models of power grids, geographic information, new energy stations, organizations, etc., mainly including basic information, static parameters, dynamic parameters, association relationships, etc.

[0041] The operating data are various types of data generated during the operation of the distribution automation system to reflect the operating status of the distribution network master station, distribution terminal, and integrated terminal equipment. They mainly include switching quantity, electricity quantity, prediction, telemetry, telesignaling, edge computing and other time series data and their derivative data, as well as alarm, fault, maintenance and other matter data.

[0042] After the principles and methods of data classification and grading are confirmed, data asset combing tools are used to automatically locate the distribution of data assets, and manual means are combined to classify and grade sensitive data, so that the distribution automation system can focus on protecting data assets according to needs.

[0043] Step 2: Based on the grading and classification results, differentiated full life cycle management is carried out for data of different levels and categories, including data collection, data transmission, data storage, data use, data exchange and data destruction.

[0044] The entire life cycle of data includes data collection, data transmission, data storage, data use, data exchange, and data destruction. During the entire life cycle of data, the principles of secure collection, secure transmission, secure storage, secure use, secure interaction, and secure destruction should be followed.

[0045] The cloud master station data collection should sort out the data to be collected, classify and grade it, and identify sensitive data. The collection plan should be planned based on the classification and grading results, and the negative list data should be encrypted during the collection process.

[0046] Data storage complies with the security requirements for commercial confidential data, important data, general data, and public data, and provides differentiated storage protection through technologies such as data encryption and storage backup.

[0047] Data interaction includes scenarios such as internal data transmission of the cloud master station of the distribution automation system, data collection and data transmission of the fusion terminal, and external interactive data transmission. Data exchange involves scenarios such as data access, external provision of data, and data publishing. In combination with the distribution network data business scenario, targeted differentiated protection can be carried out.

[0048] Step 3: Based on the data managed throughout the entire life cycle, conduct correlation risk analysis, data behavior analysis, and comprehensive analysis on the data, and perform shared data anonymity and data leakage tracing based on the analysis results.

[0049] First, for the key business data of the distribution network master station, based on data classification and entire life cycle management, correlation risk analysis is carried out, covering the two dimensions of big data analysis and correlation analysis.

[0050] The big data analysis is combined with a big data analysis engine to accurately and quickly realize real-time analysis of massive distribution network cloud master station internal and cross-system data, effectively monitor whether the business data contains abnormal risks, and prevent external risks from affecting the operation of the cloud master station business system.

[0051] The correlation analysis utilizes the correlation between important business data in the distribution network cloud master station and the inherent connection between the data to explore the associated risks of massive discrete data in the distribution network, so as to quickly associate the hidden risks between different businesses in the distribution network, reduce the detection and handling time of data security alarms in the distribution network master station, and improve the efficiency of data security handling in the distribution network.

[0052] Secondly, data behavior analysis is performed on the business data of the distribution network cloud master station. The data behavior analysis includes behavior baseline analysis and abnormal behavior detection. By extracting the behavior baseline features of the distribution network cloud master station data throughout its life cycle and training the corresponding behavior baseline model, the internal behavior habits of the cloud master station data can be mastered, and abnormal data transmission and communication behaviors can be detected using the behavior baseline to master security risks.

[0053] Starting from the perspective of data objects, in view of the characteristics of internal threats, on the basis of sensitive data content monitoring, in the face of the leakage protection needs of distribution automation system data at various stages of the life cycle, this invention proposes a data security governance framework and a user-centric internal threat detection model, based on user entity behavior analysis technology, to collect user behavior information from the terminal side and the network side, and record behavior information such as drip leakage, encrypted outbound transmission, nested files, multi-layer compression, and outbound super-large files. Through continuous profiling and modeling of user entities, and establishing a normal user behavior baseline, using contextual association analysis and other technologies, combined with data risk levels, abnormal behaviors such as data leakage are discovered from massively collected security data, and automatic response is achieved based on data sensitivity level strategies, thereby improving the timely perception and active defense capabilities of threats such as data leakage in scenarios such as business access, network transmission, and user operation and maintenance operations in the power monitoring system.

[0054] Then, a comprehensive analysis is conducted on the business data of the distribution network cloud master station to achieve shared data anonymity, and data watermarks are used to track leaked shared data.

[0055] When the distribution network cloud master station shares business data, the standardized processing flow of anonymity-watermarking is followed to prevent the abuse of important business data within the cloud master station and prevent important business data from flowing out of the cloud master station without anonymization, thereby achieving confidentiality and traceability during data transmission.

[0056] Data anonymity is used to protect the data security of the cloud master station of the distribution automation system in data processing and other links to the greatest extent, and prevent sensitive data from being illegally accessed and spread. Through the identification of sensitive data, the formulation of anonymity rules, the execution of anonymity operations, and the improvement of anonymity effect evaluation, accurate anonymity of sensitive data can be achieved.

[0057] The cloud master shared data anonymity provides a wealth of anonymity algorithms and flexible, process-based strategies, supports the anonymity processing of massive, multi-source heterogeneous data sources, and quickly implements the anonymity of important business data without changing the business process, effectively reducing the complexity and risk of anonymity, and controlling the cost of data anonymity events and the impact of latency on business systems. Data anonymity is divided into dynamic anonymity and static anonymity. Dynamic anonymity is applied to scenarios of real-time access to business data, and achieves differentiated, dynamic, and precise data anonymity based on technologies such as whitelists and SQL rewriting. Static anonymity is aimed at scenarios such as development and testing, data analysis, and external third-party data sharing, and implements large-scale data anonymity based on the set anonymity rules.

[0058] Combined with the sensitive data identification of the distribution automation system, in the key links such as sensitive information identification and sensitive algorithm selection, machine learning methods such as text classification and decision trees, as well as data desensitization algorithms such as data aggregation, anonymity technology, and differential privacy are used to evaluate the desensitization results from dimensions such as the degree of sensitive information removal, data loss, and computational overhead. A sensitive data desensitization solution suitable for the distribution automation system is formed to solve the data privacy protection problem of the distribution automation system data in data interaction scenarios such as interaction and query between applications, and meet the data interaction, sharing, and integration needs of the power monitoring system data across systems, disciplines, and industries.

[0059] The text classification is achieved by converting the text into a feature vector and using a machine learning model to perform classification prediction, thereby classifying the text data into predefined categories. First, feature extraction is performed to convert the text into a feature vector, for example, using methods such as a bag of words model, TF-IDF, word2vec, etc. Then, model training is performed, using a machine learning model such as a support vector machine (SVM), naive Bayes, neural network, etc., to train the feature vector and learn the classification function. Finally, model prediction is performed, that is, using the trained model to classify and predict the new text. Common algorithms include support vector machine (SVM), naive Bayes, neural network, decision tree, etc.

[0060] The data hiding algorithm includes data aggregation, anonymity technology and differential privacy, which can effectively protect individual privacy while ensuring the availability of data. Data aggregation can effectively prevent attackers from identifying individual data by reducing data granularity, anonymity technology can effectively prevent attackers from identifying individual data by adding random noise, and differential privacy can effectively prevent attackers from identifying individual data by adding random noise. The data aggregation includes merging multiple data points into one data point to reduce data granularity and protect individual privacy. The method is to group the data and calculate the statistics of each group, such as mean, sum, count, etc. The anonymity technology protects individual privacy by deleting or modifying sensitive information in the data. Common anonymity technologies used in the method include k-anonymity, l-diversity, t-closeness, etc. The differential privacy protects individual privacy while ensuring the availability of data during data publishing or analysis. The method is to add random noise to the data so that attackers cannot distinguish individual data.

[0061] The evaluation of desensitization results is used to select a suitable desensitization algorithm and adjust desensitization parameters to achieve the best desensitization effect. The evaluation of desensitization results needs to be carried out from the following dimensions:

[0062] (1) Degree of sensitive information removal: refers to the degree of leakage of sensitive information in desensitized data. Evaluation methods include sensitivity testing, which uses known sensitive information to test whether the desensitized data can restore the original sensitive information; and privacy leakage risk assessment, which evaluates the possibility of attackers using desensitized data to restore the original sensitive information. Privacy protection standards: For example, anonymity technologies such as k-anonymity and l-diversity can provide quantitative indicators of the degree of privacy protection.

[0063] (2) Data loss: refers to the degree of difference between the desensitized data and the original data. Evaluation methods include data quality assessment: evaluating the quality of desensitized data, such as completeness, consistency, accuracy, etc.; data availability assessment: evaluating the impact of desensitized data on downstream applications, such as data mining, data analysis, etc.; data difference analysis: calculating the difference between the data before and after desensitization, such as mean difference, variance difference, etc.

[0064] (3) Computational overhead: refers to the computing resources required for the desensitization process, such as CPU, memory, time, etc. Evaluation methods include algorithm complexity analysis: analyzing the time complexity and space complexity of the desensitization algorithm; resource consumption test: testing the consumption of computing resources by the desensitization process; performance optimization: optimizing the desensitization algorithm to reduce computational overhead.

[0065] (4) Form a sensitive data hiding scheme: Based on the above evaluation results, select a suitable desensitization algorithm and adjust the desensitization parameters to form a sensitive data hiding scheme suitable for the distribution automation system. For example: a high degree of sensitive information removal, low data loss, and low computational overhead: differential privacy or homomorphic encryption can be selected. A high degree of sensitive information removal, high data loss, and low computational overhead: data desensitization technology such as replacement and encryption can be selected. A low degree of sensitive information removal, low data loss, and low computational overhead: data aggregation or anonymization technology can be selected.

[0066] In a specific embodiment, if it is necessary to perform desensitization processing on user data in the distribution automation system to protect user privacy, the following solution may be adopted:

[0067] Identify sensitive information in user data, such as name, address, phone number, etc.;

[0068] Use known user data to test whether the desensitized data can restore the original sensitive information;

[0069] Calculate the difference between the data before and after desensitization, such as the accuracy of the name, the completeness of the address, etc.;

[0070] Test the consumption of computing resources by the desensitization process;

[0071] Based on the evaluation results, select an appropriate desensitization algorithm, such as differential privacy or homomorphic encryption;

[0072] Adjust desensitization parameters, such as the privacy budget for differential privacy or the key length for homomorphic encryption, based on data loss and computational overhead.

[0073] Through the above steps, a sensitive data hiding scheme suitable for distribution automation system can be formed to effectively protect user privacy while ensuring data availability.

[0074] The data watermark technology is used to track leaked shared data. The cloud master station supports the data watermark traceability function, which reduces the difficulty of tracing and holding accountable for the shared data of the cloud master station during distribution and use, and can accurately trace and hold accountable once a leakage incident occurs. By using highly concealed structured data watermarking technology, a watermark key is generated by extracting the characteristic information of the outgoing data, and the watermark key is implicitly added to the content of the outgoing data to ensure that it is not discovered by attackers and does not affect the use of the data. The present invention breaks through the limitations of the types and application scenarios of traditional data watermarking algorithms, and proposes a multivariate data watermarking algorithm that meets the business needs of the cloud master station. The watermark information will not be lost due to some changes in the data, thereby ensuring the integrity of the data watermark. At the same time, embedding watermark mark information in the original shared data does not affect the availability of the original data. The design of the watermark extraction rule ensures a low misjudgment rate and a low error rate for data tracing. Once a data leak occurs, the watermark key can be extracted for efficient and accurate tracing.

[0075] Specifically, the data watermark algorithm uses highly concealed structured data watermark technology to extract the characteristic information of the outgoing data to generate a watermark key, and implicitly adds the watermark key to the outgoing data content to ensure that it cannot be discovered by attackers and does not affect the use of data. The algorithm steps include:

[0076] Extract feature information from outgoing data, such as statistical features, frequency features, image features, etc.

[0077] The characteristic information is hashed to generate the watermark key.

[0078] The watermark key is implicitly added to the outgoing data content, for example, the watermark key is embedded in the redundant bits, noise bits of the data or the pixel values ​​of the image.

[0079] The watermark key is extracted from the received data and compared with the original watermark key to determine whether the data has been tampered with or leaked.

[0080] The algorithm supports a variety of feature extraction methods and watermark embedding methods, which can be selected and combined according to different business needs. The embedding method of the watermark key is highly concealed and difficult to be discovered and removed by attackers.

[0081] Robustness: The watermark algorithm is robust to data tampering and compression, and can ensure the integrity of the watermark information. The watermark algorithm supports the reversible extraction of watermark information, and the watermark key can be extracted even if the data is partially damaged.

[0082] The mathematical expression of this algorithm is:

[0083] Feature extraction: F(x) = f(x). Where F(x) is the extracted feature information, f(x) is the feature extraction function, and x is the original data.

[0084] Watermark key generation: K = H (F (x)), where K is the watermark key and H is the hash function.

[0085] Watermark embedding: x'=E(x,K), where x' is the data after watermark embedding and E is the watermark embedding function.

[0086] Watermark extraction: K'=D(x'), where K' is the extracted watermark key and D is the watermark extraction function.

[0087] Source tracing: Determine whether K'=K is satisfied. K' is the extracted watermark key, K is the original watermark key, and source tracing refers to data tracing and tracking based on the extracted watermark key.

[0088] The multivariate feature extraction method and watermark embedding method have high concealment, robustness and reversibility, and can effectively track leaked shared data and reduce the difficulty of tracing and holding accountable.

[0089] Aiming at the data flow and distribution scenarios of the power monitoring system, the present invention is based on a reversible database watermark algorithm and a data fingerprint algorithm, and uses a genetic algorithm to optimize the watermark method. The fitness function based on the genetic algorithm is the core of the genetic algorithm optimization and is used to evaluate the quality of the watermark embedding strategy. By simulating natural selection and genetic mechanisms, the genetic algorithm continuously iterates and optimizes in the population of candidate watermark embedding strategies, and finds the optimal watermark embedding strategy under given conditions, so that the fitness function achieves the maximum value, thereby achieving the best balance between embedding capacity and data distortion. The fitness function based on the genetic algorithm achieves a balance between watermark embedding capacity and data distortion, solving the problem of data leakage tracing during the data flow of the distribution automation system.

[0090] In the sharing and opening of distribution automation system data and the aggregation and processing of multi-source data, there is a risk of sensitive data being inconvenient to share and privacy data being leaked. Data sharing computing technology based on multi-party secure computing, including obfuscated circuits and secret sharing, can achieve data sharing and fusion applications among multiple subjects without leaking original data and ensuring information security, ensuring that data is dedicated and sufficient, and preventing data from being misused or abused, and solving the problem of cross-departmental and cross-enterprise sharing of sensitive data in business scenarios such as power market transactions, source-grid-load-storage. Multi-party secure computing algorithms allow multiple participants to jointly complete data calculation or analysis tasks without leaking their input data. Participants use secret sharing technology to divide their input data into multiple shares and distribute these shares to other participants. Each participant holds a part of the data of other parties, but cannot reconstruct the original data alone. After the participants complete the calculation using the shares they hold, a sufficient number of shares are required to reconstruct the final calculation results.

[0091] The present invention utilizes the zero-trust-based distribution automation system business data security access control, combined with the dispatching digital certificate system, to design a zero-trust-based data security access control framework to ensure the security, reliability and autonomous controllability of key data access. In a further embodiment, the business data security calculation of the power monitoring system based on homomorphic encryption is adopted, and the business data homomorphic encryption of the distribution automation system allows calculation on the distribution network encrypted data without decryption first. The calculation result is the same as the result calculated directly on the plaintext after decryption. The data can be processed while maintaining data privacy to achieve the legal verification and trusted audit of the secret transaction data during use. The power monitoring data is encrypted using a homomorphic encryption algorithm (such as full homomorphic encryption or partial homomorphic encryption, such as Paillier encryption) for the core business data of the distribution network. The encrypted data can be transmitted and stored without leaking the original information, and the data of multiple power monitoring points are aggregated and calculated in an encrypted state, such as summing and averaging, to perform load analysis, and perform data analysis in an encrypted state, such as distribution network pattern recognition and remote control data anomaly detection, to monitor the operation status of the distribution automation system.

[0092] The zero-trust-based data security access control framework follows the following principles:

[0093] 1) Zero Trust Principle: Do not trust any internal or external network, and assume that all network interactions are insecure.

[0094] 2) Least privilege: Users and devices are granted only the permissions necessary to complete their tasks.

[0095] 3) Continuous Authentication: Strict authentication and authorization of all access requests, regardless of their source.

[0096] The zero-trust-based data security access control framework includes:

[0097] 1) Policy Engine, which is used to define and execute access control policies, including authentication, authorization, device trust assessment, etc. The policy engine includes a policy manager, which is used to formulate and update access policies, and a decision maker, which is used to make decisions on approving or rejecting access requests based on policies.

[0098] 2) Control Plane, which is used to manage and distribute policies, monitor and record access behavior. The control plane includes a policy distributor, which communicates the decisions of the policy engine to the execution point, and a logging and monitoring component, which records all access requests and events for auditing and analysis.

[0099] 3) Data Plane: used to implement access control policies and protect data resources. The data plane includes access agents, which act as intermediaries between users and resources to implement access control.

[0100] Micro-segmentation, which is used to divide the network into small secure zones to limit lateral movement.

[0101] 4) Identity and Device Authentication component, which is used to verify the identity of users and devices. This component includes a multi-factor authentication (MFA) component and a device trust assessment component.

[0102] The workflow of the zero-trust-based data security access control framework is as follows: A user attempts to access a resource. The user's identity is verified, including user credentials and multi-factor authentication. The device from which the request is initiated is checked for compliance and security. Contextual information of the user and device is collected, such as geographic location, timestamp, access frequency, etc. The policy engine makes authorization decisions based on access policies, authentication results, device assessments, and contextual information. If authorization is granted, the access agent allows the user to access the resource; otherwise, access is denied. All access requests and decisions are recorded for auditing and subsequent analysis.

[0103] Adjust access permissions based on real-time contextual information. Implement the principle of least privilege and grant only necessary access.

[0104] All access behaviors are logged in detail to facilitate auditing and compliance checks. Security policies can be automatically adjusted based on detected threat patterns.

[0105] The beneficial effect of the present invention is that, compared with the prior art, the present invention provides a method and system for hiding shared data in a distribution automation system, which ensures the confidentiality and traceability of key distribution network business data during sharing and transmission, realizes situational awareness of data threats, and enhances the security protection capability of shared data of the distribution automation system cloud master station.

[0106] The present invention may be a system, a method and / or a computer program product. The present invention also discloses a system for hiding shared data in a distribution automation system based on the aforementioned method for hiding shared data in a distribution automation system, comprising:

[0107] A grading and classification module is used to obtain the distribution automation system cloud master station data, grade and classify the distribution automation system cloud master station data, and divide the sensitive data range;

[0108] The full life cycle management module is used to perform differentiated full life cycle management of data of different levels and categories based on the classification and classification results, including data collection, data transmission, data storage, data use, data exchange and data destruction;

[0109] The data analysis module is used to perform correlation risk analysis, data behavior analysis and comprehensive analysis on the data managed throughout the entire life cycle, and to anonymize shared data and trace data leakage based on the analysis results.

[0110] Based on the spirit of the present invention, those skilled in the art can easily think that a computer program product can be obtained based on the aforementioned method for hiding shared data in a distribution automation system. The computer program product may include a computer-readable storage medium on which computer-readable program instructions for enabling a processor to implement various aspects of the present disclosure are loaded. That is, the present application also includes a terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to perform the steps according to the aforementioned method for hiding shared data in a distribution automation system.

[0111] Computer readable storage medium can be a tangible device that can keep and store the instructions used by the instruction execution device. Computer readable storage medium can be, for example, - but not limited to - electrical storage device, magnetic storage device, optical storage device, electromagnetic storage device, semiconductor storage device or any suitable combination of the above. More specific examples (non-exhaustive list) of computer readable storage medium include: portable computer disk, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanical encoding device, for example, punch card or groove protrusion structure with instructions stored thereon and any suitable combination of the above. Computer readable storage medium used here is not interpreted as instantaneous signal itself, such as radio wave or other free propagating electromagnetic wave, electromagnetic wave propagated by waveguide or other transmission medium (for example, light pulse by optical fiber cable) or electrical signal transmitted by wire.

[0112] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0113] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages-such as Smalltalk, C++, etc., and conventional procedural programming languages-such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network-including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA) may be personalized by utilizing the state information of a computer-readable program instruction, and the electronic circuit may execute a computer-readable program instruction, thereby realizing various aspects of the present disclosure.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for hiding shared data in a distribution automation system, characterized in that: The following steps are involved: Obtaining distribution automation system cloud master station data, grading and classifying the distribution automation system cloud master station data, and dividing the sensitive data range; Based on the classification and grading results, differentiated full life cycle management is carried out for data of different levels and categories, including data collection, data transmission, data storage, data use, data exchange and data destruction; Based on the data managed throughout the entire life cycle, correlation risk analysis, data behavior analysis and comprehensive analysis are performed on the data, and shared data anonymity and data leakage tracing are performed based on the analysis results.

2. The method for hiding shared data in a distribution automation system according to claim 1, characterized in that: The grading of the distribution automation system cloud master station data includes dividing the data into commercial confidential data, important data, general data and public data; the classification of the distribution automation system cloud master station data includes dividing the data into model data and operation data.

3. The method for hiding shared data in a distribution automation system according to claim 2, characterized in that: The associated risk analysis of the data includes: Combined with the big data analysis engine, it conducts real-time analysis of massive data within the distribution network cloud master station and across systems, and monitors whether the business data contains abnormal risks; The correlation between business data in the distribution network cloud master station and the inherent connection between data are analyzed to discover the associated risks of massive discrete data in the distribution network and to associate the hidden risks between different businesses in the distribution network.

4. The method for hiding shared data in a distribution automation system according to claim 3, characterized in that: The data anonymization includes dynamic anonymization and static anonymization. The dynamic anonymization is applied to the scenario of real-time access to business data, and the data is differentiated and anonymized based on whitelist and SQL rewriting technology; the static anonymization is applied to development testing, data analysis, and external third-party data sharing scenarios, and batch data anonymization is achieved according to the set anonymization rules; The shared data concealment based on the analysis results includes: Combined with the sensitive data identification of the distribution automation system, in the process of sensitive information identification and sensitive algorithm selection, machine learning methods and data desensitization algorithms are used to evaluate the desensitization results from the dimensions of sensitive information removal degree, data loss, and computing overhead, to form a sensitive data desensitization solution suitable for the distribution automation system.

5. A system for hiding shared data in a distribution automation system, characterized in that: include: A grading and classification module is used to obtain the distribution automation system cloud master station data, grade and classify the distribution automation system cloud master station data, and divide the sensitive data range; The full life cycle management module is used to perform differentiated full life cycle management of data of different levels and categories based on the classification and classification results, including data collection, data transmission, data storage, data use, data exchange and data destruction; The data analysis module is used to perform correlation risk analysis, data behavior analysis and comprehensive analysis on the data managed throughout the entire life cycle, and to anonymize shared data and trace data leakage based on the analysis results.

6. The system for hiding shared data in a distribution automation system according to claim 5, characterized in that: The grading and classification module is further used for: Divide data into confidential data, important data, general data and public data; Divide the data into model data and operational data.

7. The system for hiding shared data in a power distribution automation system according to claim 6, characterized in that: The data analysis module is further used for: Combined with the big data analysis engine, it conducts real-time analysis of massive data within the distribution network cloud master station and across systems, and monitors whether the business data contains abnormal risks; The correlation between business data in the distribution network cloud master station and the inherent connection between data are used for analysis to discover the associated risks of massive discrete data in the distribution network, so as to associate the hidden risks between different businesses in the distribution network.

8. The system for hiding shared data in a power distribution automation system according to claim 7, characterized in that: The data anonymization includes dynamic anonymization and static anonymization. The dynamic anonymization is applied to the scenario of real-time access to business data, and the data is differentiated and anonymized based on whitelist and SQL rewriting technology; the static anonymization is applied to development testing, data analysis, and external third-party data sharing scenarios, and batch data anonymization is achieved according to the set anonymization rules; The data analysis module is further used for: Combined with the sensitive data identification of the distribution automation system, in the process of sensitive information identification and sensitive algorithm selection, machine learning methods and data desensitization algorithms are used to evaluate the desensitization results from the dimensions of sensitive information removal degree, data loss, and computing overhead, to form a sensitive data desensitization solution suitable for the distribution automation system.

9. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the steps of the method for hiding shared data in a distribution automation system according to any one of claims 1 to 4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method for hiding shared data in a distribution automation system as described in any one of claims 1 to 4 are implemented.

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