E-government data access control method and system
By generating authorization decisions and dynamically updating the access list in conjunction with multi-dimensional information, the problem of fixed permissions in e-government data access control methods is solved, achieving efficient and secure dynamic access control and improving accuracy and efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HUBEI ENG UNIV
- Filing Date
- 2026-01-09
- Publication Date
- 2026-04-21
AI Technical Summary
In existing technologies, e-government data access control methods have fixed permissions and cannot adapt to dynamic changes in user roles and context, resulting in low accuracy and efficiency.
By acquiring government data and access data, authorization decisions are generated. Combining multi-dimensional information such as user identity, access time, IP address, and operation intent, an authorization decision recommendation model is constructed using the LightGBM model. The access chain is dynamically updated, and blockchain technology is used to ensure that access records are tamper-proof and traceable.
It enables fine-grained, intelligent, and dynamic access control for government data, enhances security protection capabilities and response efficiency, improves the accuracy of authorization and the scientific nature of risk assessment, and ensures the authenticity and credibility of access records.
Smart Images
Figure CN121902176A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of information security technology, specifically an e-government data access control method and system. Background Technology
[0002] With the rapid development of information technology and the deepening of government digital transformation, e-government systems are playing an increasingly important role in improving administrative efficiency, optimizing public services, and promoting government transparency. The massive amounts of government data collected, stored, shared, and applied have formed a vast information resource system covering multiple fields such as population, legal entities, natural resources, and macroeconomics. However, the widespread sharing and openness of data has also brought serious security challenges, especially the escalating risks of sensitive information leakage, unauthorized access, and internal abuse.
[0003] Traditional access control methods, such as Discretionary Access Control (DAC) and Mandatory Access Control (MAC), are typically static. Once permissions are granted, they are unlikely to change unless manually modified by the administrator. This makes them unsuitable for adapting to the dynamic changes in user roles and contexts in e-government data access scenarios, resulting in low accuracy and efficiency of e-government data access control methods. Therefore, further improvements are needed for e-government data access control methods. Summary of the Invention
[0004] This application aims to address at least one of the technical problems existing in the prior art; to this end, this application proposes an e-government data access control method and system to solve the technical problem that access control permissions in the prior art are often fixed and cannot adapt to the dynamic changes in user roles and context in e-government data access scenarios, resulting in low accuracy and efficiency of e-government data access control methods.
[0005] To achieve the above objectives, the first aspect of this application provides an e-government data access control method, comprising: Acquiring government data and accessing data; the government data refers to the data that the user needs to access; the access data refers to the relevant data generated when the user accesses the data. Authorization decisions are generated based on access data; these decisions include allowing access, denying access, and requiring de-identification of access. Access records are generated based on government data and authorization decisions; these access records refer to relevant access data generated when a user accesses government data. The access list is dynamically updated based on access records.
[0006] This application, through the aforementioned steps, comprehensively utilizes multi-dimensional information from access data and government data, breaking through the traditional static authorization model based on user identity. In the authorization decision-making process, it not only considers user identity attributes but also integrates rich contextual information such as access time, IP address, request operation type, and access frequency, constructing a dynamic evaluation mechanism based on the real-time environment. This enables access control decisions to move beyond simple allow or deny, allowing for adaptive adjustments based on actual access scenarios. It achieves fine-grained, intelligent, and dynamic access control for government data, significantly improving the security protection capabilities, authorization accuracy, and response efficiency of e-government systems in complex environments.
[0007] Furthermore, the generation of authorization decisions based on access data includes: Extract the subject attributes, environment attributes, and operation intent from the access data; the subject attributes refer to the subject parameters requesting access, including user ID, department, and job title; the environment attributes refer to the environmental state at the time of requesting access, including access time, visitor IP, terminal security status, and access method; the operation intent refers to the behavioral data that the user ID wants to perform, including Read, Write, Export, and Share; Extract the object attributes corresponding to government data; the object attributes refer to the attribute characteristics corresponding to the government data that the user ID wants to access, including data ID, data security level, department, data type, sensitive fields, and desensitization rules; The request analysis data is obtained by combining the subject attributes, object attributes, environmental attributes, and operational intent. The request analysis data is input into the authorization decision recommendation model to obtain the authorization decision; the authorization decision recommendation model is constructed using the LightGBM model, which is used to determine the decision category of the request analysis data, thereby obtaining the authorization decision when the current user ID accesses government data.
[0008] Furthermore, the authorization decision recommendation model is constructed using the LightGBM model, including: Obtain and analyze historical request data and their corresponding historical authorization decisions; Several historical request analysis data and their corresponding historical authorization decisions are divided into training data, validation data, and test data; and the training data, validation data, and test data are preprocessed to obtain training set, validation set, and test set; Obtain a pre-trained LightGBM model; The baseline model is obtained by training a pre-trained LightGBM model on the training set. The optimal parameter combination is obtained by performing hyperparameter tuning on the validation set using the Bayesian optimization algorithm on the baseline model. The baseline model, after hyperparameter tuning, is tested on the test set to obtain the accuracy and F1 score. When both accuracy and F1 score meet the preset conditions, the baseline model after hyperparameter tuning will be used as the authorization decision recommendation model.
[0009] This application constructs request analysis data based on multiple features in government data and access data using a pre-trained authorization decision recommendation model. The authorization decision recommendation model is then used to conduct a multi-dimensional comprehensive analysis of the request analysis data to determine the authorization decision. This improves the accuracy and efficiency of authorization decision generation and avoids the problem of low accuracy and efficiency of access control methods for government data caused by fixed authorization decisions. It provides accurate data support for subsequent access control of government data.
[0010] Furthermore, the generation of access records based on government data and authorization decisions includes: Extract the allowed access, denied access, and access requiring de-identification from the authorization decision; When the authorization decision is to deny access or allow access, the access operation data is extracted; the access operation data includes user ID, access time, accessed data resources, executed operation behavior, and access result; the access includes successful access and failed access; When the authorization decision requires de-identified access, de-identified government data is generated based on the access data and government data. Extract user ID access operation data for de-identified government data; the access operation data includes user ID, access time, accessed data resources, executed operation behavior, and access result; the access result includes de-identified access. Access operation data is recorded as access records.
[0011] Furthermore, the generation of desensitized government data based on access data and government data includes: Extract the anonymization rules corresponding to government data; An access risk score is generated based on access data and government data; Extract the risk threshold and desensitization strategy from the desensitization rules; the desensitization strategy includes strong desensitization strategy and weak desensitization strategy; When the access risk score is greater than or equal to the risk threshold, the government data will be desensitized according to the strong desensitization strategy to obtain the corresponding desensitized government data. When the access risk score is less than the risk threshold, the government data will be desensitized according to a weak desensitization strategy to obtain the corresponding desensitized government data.
[0012] Furthermore, the generation of access risk scores based on access data and government data includes: Extract the data security level from the object attributes corresponding to government data; Extract the department corresponding to the subject attribute, the access time and visitor IP corresponding to the environmental attribute, and the operation intent from the access data; The data sensitivity score (SMF) is obtained from the data sensitivity table based on the data security level; the data sensitivity table is set by experts based on experience. The operational risk score (CFF) is obtained from the operational risk table based on the operational intent; the operational risk table is set by experts based on experience. The user trust score YXF is obtained from the user trust table by the relevant department; the user trust table is set by experts based on experience. Through formula Calculate the Environmental Risk Factor (HFP); where, This is represented as a basic environmental score, obtained from a basic scoring table based on the visitor's IP address. The basic scoring table is set by experts based on experience. This is represented as a dynamic environmental score, determined by the access time; The access risk score is calculated using a formula; the formula satisfies: ;in, , , and Represented as weighting coefficients, , , and ∈(0,1), and The specific values are set based on experience. This is represented as a penalty score.
[0013] Furthermore, the penalty score is obtained through the following methods: Extract data sensitivity scores, operational risk scores, user trust scores, and environmental risk scores; Data sensitivity scores, operational risk scores, user trust scores, and environmental risk scores are combined to form penalty analysis data; The penalty analysis data is input into the score analysis model to obtain the penalty score; the score analysis model is constructed through an artificial intelligence model and is used to analyze the penalty analysis data to obtain the risk penalty score when the current user ID accesses government data; The score analysis model is constructed using an artificial intelligence model, including: Obtain some historical penalty analysis data and their corresponding historical penalty scores; Several historical penalty analysis data and their corresponding historical penalty scores are divided into training data, validation data, and test data; and the training data, validation data, and test data are preprocessed to obtain the training set, validation set, and test set. Choose an artificial intelligence model as the base model; The base model is trained on the training set, and the learning rate and other hyperparameters are adjusted on the validation set to obtain the pre-trained model. By validating the pre-trained model on the test set, a score analysis model is finally obtained, which takes penalty analysis data as input and outputs penalty scores.
[0014] This application conducts multi-dimensional in-depth analysis of government data and access data. In scenarios where authorization decisions determine that anonymized access is required, it identifies and extracts key indicators affecting access risk, and constructs a risk assessment system for data security. Based on a pre-trained score analysis model, it performs comprehensive modeling and correlation analysis on the above indicators to determine whether a specific combination of indicators may lead to an increase in access risk, and quantifies the penalty score reflecting the degree of risk change accordingly. By introducing a collaborative mechanism of weighting coefficients, nonlinear scoring functions, and dynamic penalty terms, the application enhances the model's ability to characterize complex and nonlinear real-world access scenarios, making the access risk score closer to the actual security situation and improving the scientific rigor and rationality of the risk assessment. Based on this, the calculated access risk score is compared with the preset dynamic risk threshold, triggering the corresponding desensitization strategy execution mechanism to achieve adaptive desensitization processing of sensitive government data. That is, the desensitization intensity is automatically increased in high-risk scenarios and the desensitization granularity is appropriately reduced in low-risk scenarios, taking into account both data availability and security. This not only realizes the dynamic and precise control of the desensitization strategy, but also significantly improves the security guarantee capability of government data in the process of open sharing, and further optimizes the accuracy and intelligence level of the e-government access control mechanism.
[0015] Furthermore, the dynamic updating of the access list based on access records includes: Extract the access records and perform structured operations on them to obtain the access structure records; The hash value H1 is obtained by performing a hash function on the access structure record; the hash value has the characteristics of fixed length and uniqueness. A transaction is generated by calling a specific method of the smart contract on the access list; the specific method of the smart contract is the addRecord(H1) method; and the access list is constructed using blockchain technology. Valid transactions that have undergone transaction distribution and consensus verification are packaged into a new block; the new block contains the hash values of multiple access record transactions; When a new block is successfully added to the access list, the smart contract generates a transaction receipt to dynamically update the access list; the transaction receipt includes the transaction hash, block hash, and block height.
[0016] To ensure the authenticity and credibility of access records, this application utilizes the immutability and traceability of blockchain to package all access records into transactions. After consensus is reached, these transactions are stored in a dedicated access chain, making it impossible for anyone to tamper with or delete records individually. In the event of a security incident or data breach, auditors can quickly and reliably trace the entire access chain, accurately identify the responsible party, and improve the scientific rationality and security of government data access control.
[0017] A second aspect of the present invention provides an e-government data access control system, comprising: a data acquisition module and a data analysis module; the data acquisition module and the data analysis module are connected together; The data acquisition module acquires government data and access data through data acquisition equipment; the government data refers to the data that users need to access; the access data refers to the relevant data generated when users access data. The data analysis module includes a decision generation unit, a record generation unit, and a linked list update unit; The decision generation unit generates authorization decisions based on access data; The record generation unit generates access records based on government data and authorization decisions. The linked list update unit dynamically updates the access linked list based on the access records.
[0018] Another aspect of the present invention provides a computer-readable storage medium on which a computer program is stored, which, when executed by a processor, implements an e-government data access control system according to the second aspect of the present invention.
[0019] Compared with the prior art, the beneficial effects of this application are: 1. This application generates authorization decisions based on access data; generates access records based on government data and authorization decisions; and dynamically updates the access list based on access records. By using multi-dimensional data in access data and government data, including not only user identity but also contextual information such as access time, IP address, requested operation type, and access frequency, authorization decisions are no longer a simple yes or no, but can be dynamically adjusted according to real-time context. This achieves dynamic access control of government data and improves the accuracy and efficiency of e-government data access control methods.
[0020] 2. This application analyzes government data and access data from multiple dimensions. When authorization decisions require de-identification of access, it identifies several indicators needed to quantify access risk scoring. A pre-trained score analysis model is used to comprehensively analyze these indicators, determining whether a combination of indicators leads to increased access risk. This yields a factor quantifying the degree of increased access risk. By combining weighting coefficients, non-linear scoring, and penalty terms, the application enhances the ability to characterize complex real-world scenarios, making risk assessment results more scientific and reasonable. This provides a precise basis for adaptive de-identification of government data. By comparing access risk scores with corresponding risk thresholds, the application dynamically performs adaptive de-identification operations on government data requiring de-identification, improving the security of government data access and the accuracy of government data access control methods.
[0021] 3. To ensure the authenticity and credibility of access records, this application utilizes the immutability and traceability of blockchain to package all access records into transactions. After consensus, these transactions are stored in a dedicated access chain, making it impossible for anyone to tamper with or delete records individually. In the event of a security incident or data breach, auditors can quickly and reliably trace the entire access chain, accurately identify the responsible party, and improve the scientific rationality and security of government data access control. Attached Figure Description
[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0023] Figure 1 This is a flowchart of an e-government data access control method according to this application; Figure 2 This is a schematic diagram of the principle of an e-government data access control system according to this application. Detailed Implementation
[0024] The technical solutions of this application will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.
[0025] Please see Figure 1 The first aspect of this application provides an e-government data access control method, including: Acquiring government data and accessing data; government data refers to the data that users need to access; access data refers to the relevant data generated when users access data. Authorization decisions are generated based on access data; authorization decisions include allowing access, denying access, and requiring de-identification of access. Access records are generated based on government data and authorization decisions; access records refer to relevant access data generated when users access government data. The access list is dynamically updated based on access records.
[0026] In this embodiment, the authorization decision generation based on access data includes: Extract the subject attributes, environment attributes, and operation intent from the access data; subject attributes refer to the parameters of the subject requesting access, including user ID, department, and job title; environment attributes refer to the environmental state at the time of requesting access, including access time, visitor IP, terminal security status, and access method; operation intent refers to the behavioral data of the user ID that wants to perform the operation, including Read, Write, Export, and Share; Extract the object attributes corresponding to government data; object attributes refer to the attribute characteristics of the government data that the user ID wants to access, including data ID, data security level, department, data type, sensitive fields, and desensitization rules; The request analysis data is obtained by combining the subject attributes, object attributes, environmental attributes, and operational intent. The request analysis data is input into the authorization decision recommendation model to obtain the authorization decision. The authorization decision recommendation model is constructed using the LightGBM model to determine the decision category of the request analysis data, thereby obtaining the authorization decision when the current user ID accesses government data.
[0027] The authorization decision recommendation model in this embodiment is constructed using the LightGBM model, including: Obtain and analyze historical request data and their corresponding historical authorization decisions; Several historical request analysis data and their corresponding historical authorization decisions are divided into training data, validation data, and test data; and data preprocessing is performed on the training data, validation data, and test data to obtain training set, validation set, and test set; the ratio between training set, test set, and validation set is 7:1.5:1.5; in this embodiment, the purpose of data preprocessing is to clean and transform the request analysis data into numerical features that the LightGBM model can understand. Obtain a pre-trained LightGBM model; The baseline model is obtained by training a pre-trained LightGBM model on the training set. The optimal parameter combination is obtained by performing hyperparameter tuning on the validation set using the Bayesian optimization algorithm on the baseline model. In this embodiment, the parameter combination includes core parameters such as num_leaves, learning_rate, n_estimators, subsample / colsample_bytree, and class_weight. The baseline model, after hyperparameter tuning, is tested on the test set to obtain the accuracy and F1 score. When both the accuracy and F1 score meet the preset conditions, the baseline model after hyperparameter tuning is used as the authorized decision recommendation model. In this embodiment, the preset conditions are that the accuracy exceeds the accuracy threshold and the F1 score exceeds the F1 threshold. The accuracy threshold and F1 threshold are set according to experience. In this embodiment, the accuracy threshold and F1 threshold are set to 0.93 and 0.91, respectively.
[0028] This embodiment utilizes a pre-trained authorization decision recommendation model to fully mine multi-source feature information from government data and access information, constructing request analysis data that includes user attributes, operational behaviors, access environments, and other elements. The model then performs multi-dimensional and in-depth comprehensive analysis of this request analysis data, enabling intelligent evaluation of access requests and generation of authorization suggestions. Compared to traditional fixed rules or static strategies, this method significantly improves the accuracy and efficiency of authorization decisions. Through a model-driven intelligent decision-making mechanism, it provides highly reliable data support for subsequent dynamic access control of government data, enhancing the system's security and adaptability in complex application scenarios.
[0029] In this embodiment, the generation of access records based on government data and authorization decisions includes: Extract the allowed access, denied access, and access requiring de-identification from the authorization decision; When the authorization decision is to deny access or allow access, the access operation data is extracted; the access operation data includes user ID, access time, accessed data resources, executed operation behavior, and access result; access includes successful access and failed access; When the authorization decision requires de-identified access, de-identified government data is generated based on the access data and government data. Extract user ID access operation data for de-identified government data; access operation data includes user ID, access time, accessed data resources, executed operation behavior, and access result; in this embodiment, the accessed data resources in the access operation data for accessible government data refer to the data after de-identification, not the original government data; access result includes de-identified access; Access operation data is recorded as access records.
[0030] In this embodiment, the generation of desensitized government data based on access data and government data includes: Extract the anonymization rules corresponding to government data; An access risk score is generated based on access data and government data; Extract the risk threshold and desensitization strategy from the desensitization rules; the desensitization strategy includes strong desensitization strategy and weak desensitization strategy; in this embodiment, the strong desensitization strategy includes full masking and irreversible hashing, etc.; the weak desensitization strategy includes partial masking and generalization, etc. When the access risk score is greater than or equal to the risk threshold, the government data will be desensitized according to the strong desensitization strategy to obtain the corresponding desensitized government data. When the access risk score is less than the risk threshold, the government data will be desensitized according to a weak desensitization strategy to obtain the corresponding desensitized government data.
[0031] In this embodiment, the generation of access risk scores based on access data and government data includes: Extract the data security level from the object attributes corresponding to government data; Extract the department corresponding to the subject attribute, the access time and visitor IP corresponding to the environmental attribute, and the operation intent from the access data; The data sensitivity score (SMF) is obtained from the data sensitivity table based on the data security level. The data sensitivity table is set by experts based on experience, and the data sensitivity table in this embodiment is shown below:
[0032] The operational risk score (CFF) is obtained from the operational risk table based on the operational intent. The operational risk table is set by experts based on experience, and the operational risk table in this embodiment is shown below:
[0033] The user trust score YXF is obtained from the user trust table by the relevant department; the user trust table is set by experts based on experience, and the user trust table in this embodiment is shown below:
[0034] Through formula Calculate the Environmental Risk Factor (HFP); where, This is represented as a basic environmental score, obtained from a basic scoring table based on the visitor's IP address. The basic scoring table is set by experts based on experience. This is represented as a dynamic environmental score, determined by the access time; in this embodiment, when the access time is outside of working hours, Set to 0.3; when access time is outside of working hours, Set to 0; the basic scoring table in this embodiment is shown below:
[0035] The access risk score is calculated using a formula; the formula satisfies: ;in, , , and Represented as weighting coefficients, , , and ∈(0,1), and The specific values are set based on experience; in this embodiment, they will be... , , and Set them to 0.4, 0.2, 0.1, and 0.3 respectively; The numerical value is set relatively high because government data itself is the most critical factor, therefore the corresponding SMF needs to be adjusted accordingly. The setting is relatively large; considering that user trust is a negative factor, its high weight can effectively reduce the risk score. It is represented as a penalty score; through the combination of weighting coefficients, non-linear scoring and penalty items, the ability to depict complex real-world scenarios is greatly enhanced, making the risk assessment results more scientific and reasonable, thus providing a precise basis for the adaptive desensitization of government data.
[0036] The penalty score in this embodiment is obtained through the following methods: Extract data sensitivity scores, operational risk scores, user trust scores, and environmental risk scores; Data sensitivity scores, operational risk scores, user trust scores, and environmental risk scores are combined to form penalty analysis data; The penalty analysis data is input into the score analysis model to obtain the penalty score; the score analysis model is constructed through an artificial intelligence model to analyze the penalty analysis data and obtain the risk penalty score when the current user ID accesses government data; The score analysis model is constructed using an artificial intelligence model, including: Obtain some historical penalty analysis data and their corresponding historical penalty scores; Several historical penalty analysis data and their corresponding historical penalty scores are divided into training data, validation data, and test data; and the training data, validation data, and test data are preprocessed to obtain training set, validation set, and test set; the ratio between training set, test set, and validation set is 7:2:1; An artificial intelligence model is selected as the base model; in this embodiment, a neural network model is selected as the artificial intelligence model. The base model is trained on the training set, and the learning rate and other hyperparameters are adjusted on the validation set to obtain the pre-trained model. By validating the pre-trained model on the test set, a score analysis model is finally obtained, which takes penalty analysis data as input and outputs penalty scores.
[0037] The dynamic updating of the access list based on access records in this embodiment includes: Access records are extracted and structured to obtain access structure records. In this embodiment, the structured operation refers to converting the raw data of access records into standard, structured data objects to facilitate subsequent operations. In this embodiment, the access structure records are in JSON format. The hash value H1 is obtained by calculating the hash value of the access structure record using a cryptographic hash function. The hash value has the characteristics of fixed length and uniqueness. Considering that any small modification to the access record will cause the calculated hash value to change greatly and unpredictably, this embodiment stores the hash value instead of the access record, which ensures both efficiency and that any tampering with the access record will be detected immediately. A transaction is generated by calling a specific method of the smart contract on the access list; in this embodiment, the specific method of the smart contract is the addRecord(H1) method; and the access list is constructed using blockchain technology. Valid transactions, after being distributed and verified through consensus, are packaged into a new block. The new block contains the hash values of multiple access record transactions. In this embodiment, transaction distribution refers to sending a transaction to a blockchain node, which then broadcasts the transaction to the entire network. Consensus verification refers to verifying the validity of a transaction using a consensus algorithm: checking whether the signature is valid, whether the format is correct, etc. Transactions that pass verification are called valid transactions. Consensus algorithms include PBFT and Raft algorithms, etc. When a new block is successfully added to the access list, the smart contract generates a transaction receipt to dynamically update the access list; the transaction receipt includes the transaction hash, block hash, and block height.
[0038] To ensure the authenticity and non-repudiation of access records, this embodiment fully leverages the core characteristics of blockchain technology, such as immutability and traceability. Each access to government data is encapsulated as a structured transaction, verified by a trusted consensus mechanism, and then sequentially written into a dedicated access chain. This ensures the integrity and persistence of access records, preventing any individual or organization from tampering with or deleting the stored records, fundamentally eliminating the risks of log forgery and subsequent repudiation. In the event of a security incident or suspected data breach, auditors can efficiently and reliably trace the complete access path based on the blockchain record, accurately reconstructing key information such as access time, subject identity, operational behavior, and context. This enables rapid identification of the responsible party and attribution of behavior, enhancing not only the auditing capabilities and transparency of the e-government system but also further improving the scientific rigor, credibility, and security of the access control system. This provides solid technical support for building a comprehensive, traceable, and strongly supervised government data governance system.
[0039] Please see Figure 2 A second aspect of this application provides an e-government data access control system, comprising: a data acquisition module and a data analysis module; the data acquisition module and the data analysis module are connected. Data acquisition module: Acquires government data and access data through data acquisition equipment; government data refers to the data that users need to access; access data refers to the relevant data generated when users access data; data acquisition equipment includes several sensors, etc. The data analysis module includes a decision generation unit, a record generation unit, and a linked list update unit; Decision generation unit: Generates authorization decisions based on access data; Record generation unit: Generates access records based on government data and authorization decisions; Linked list update unit: Dynamically update the accessed linked list based on the access record.
[0040] Another embodiment of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements an e-government data access control system according to a second aspect embodiment of this application.
[0041] Some of the data in the above formula are calculated by removing dimensions and taking their numerical values. The formula is the closest to the real situation obtained by software simulation of a large amount of collected data. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.
[0042] The working principle of this application is as follows: It acquires government data and access data; generates authorization decisions based on the access data; generates access records based on the government data and authorization decisions; and dynamically updates the access list based on the access records. By utilizing multi-dimensional data from the access data and government data—including not only user identity but also contextual information such as access time, IP address, requested operation type, and access frequency—the authorization decision is no longer a simple yes or no, but can be dynamically adjusted according to the real-time context. This achieves dynamic access control of government data, improving the accuracy and efficiency of e-government data access control methods. It avoids the problem in existing technologies where access control permissions are often fixed and cannot adapt to the dynamic changes in user roles and contextual environments in government data access scenarios, resulting in low accuracy and efficiency of e-government data access control methods.
[0043] The above embodiments are only used to illustrate the technical methods of this application and are not intended to limit it. Although this application has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of this application without departing from the spirit and scope of the technical methods of this application.
Claims
1. A method for controlling access to e-government data, characterized in that, include: Acquiring government data and accessing data; the government data refers to the data that the user needs to access; the access data refers to the relevant data generated when the user accesses the data. Authorization decisions are generated based on access data; these decisions include allowing access, denying access, and requiring de-identification of access. Access records are generated based on government data and authorization decisions; these access records refer to relevant access data generated when a user accesses government data. The access list is dynamically updated based on access records.
2. The e-government data access control method according to claim 1, characterized in that, The authorization decision generation based on access data includes: Extract the subject attributes, environment attributes, and operation intent from the accessed data; Extracting the object attributes corresponding to government data; The request analysis data is obtained by combining the subject attributes, object attributes, environmental attributes, and operational intent. The request analysis data is input into the authorization decision recommendation model to obtain the authorization decision; the authorization decision recommendation model is constructed using the LightGBM model and is used to determine the decision category of the request analysis data, thereby obtaining the authorization decision when the current user ID accesses government data.
3. The e-government data access control method according to claim 2, characterized in that, The authorization decision recommendation model is constructed using the LightGBM model, including: Obtain and analyze historical request data and their corresponding historical authorization decisions; Several historical request analysis data and their corresponding historical authorization decisions are divided into training data, validation data, and test data; and the training data, validation data, and test data are preprocessed to obtain training set, validation set, and test set; Obtain a pre-trained LightGBM model; The baseline model is obtained by training a pre-trained LightGBM model on the training set. The optimal parameter combination is obtained by performing hyperparameter tuning on the validation set using the Bayesian optimization algorithm on the baseline model. The baseline model, after hyperparameter tuning, is tested on the test set to obtain the accuracy and F1 score. When both accuracy and F1 score meet the preset conditions, the baseline model after hyperparameter tuning will be used as the authorization decision recommendation model.
4. The e-government data access control method according to claim 1, characterized in that, The generation of access records based on government data and authorization decisions includes: Extract the allowed access, denied access, and access requiring de-identification from the authorization decision; When the authorization decision is to deny access or allow access, the access operation data is extracted; the access operation data includes user ID, access time, accessed data resources, executed operation behavior, and access result; the access includes successful access and failed access; When the authorization decision requires de-identified access, de-identified government data is generated based on the access data and government data. Extract user ID access operation data for de-identified government data; the access operation data includes user ID, access time, accessed data resources, executed operation behavior, and access result; the access result includes de-identified access. Access operation data is recorded as access records.
5. The e-government data access control method according to claim 4, characterized in that, The generation of desensitized government data based on access data and government data includes: Extract the anonymization rules corresponding to government data; An access risk score is generated based on access data and government data; Extract the risk threshold and desensitization strategy from the desensitization rules; the desensitization strategy includes strong desensitization strategy and weak desensitization strategy; When the access risk score is greater than or equal to the risk threshold, the government data will be desensitized according to the strong desensitization strategy to obtain the corresponding desensitized government data. When the access risk score is less than the risk threshold, the government data will be desensitized according to a weak desensitization strategy to obtain the corresponding desensitized government data.
6. The e-government data access control method according to claim 5, characterized in that, The generation of access risk scores based on access data and government data includes: Extract the data security level from the object attributes corresponding to government data; Extract the department corresponding to the subject attribute, the access time and visitor IP corresponding to the environmental attribute, and the operation intent from the access data; Obtain the Data Sensitivity SMF score from the data sensitivity table based on the data security level; The operational risk score (CFF) is obtained from the operational risk table based on the operational intent. The user trust score YXF is obtained from the user trust table by the relevant department; the data sensitivity table, operational risk table and user trust table are all set by experts based on experience. Through formula Calculate the Environmental Risk Factor (HFP); where, This is represented as a basic environmental score, obtained from a basic scoring table based on the visitor's IP address. This basic scoring table is set by experts based on experience. This is represented as a dynamic environmental score, determined by the access time; The access risk score is calculated using a formula; the formula satisfies: ;in, , , and Represented as weighting coefficients, , , and ∈(0,1), and ; This is represented as a penalty score.
7. The e-government data access control method according to claim 6, characterized in that, The penalty score is obtained through the following methods: Extract data sensitivity scores, operational risk scores, user trust scores, and environmental risk scores; Data sensitivity scores, operational risk scores, user trust scores, and environmental risk scores are combined to form penalty analysis data; The penalty analysis data is input into the score analysis model to obtain the penalty score; the score analysis model is constructed through an artificial intelligence model and is used to analyze the penalty analysis data to obtain the risk penalty score when the current user ID accesses government data; The score analysis model is constructed using an artificial intelligence model, including: Obtain some historical penalty analysis data and their corresponding historical penalty scores; Several historical penalty analysis data and their corresponding historical penalty scores are divided into training data, validation data, and test data; and the training data, validation data, and test data are preprocessed to obtain the training set, validation set, and test set. Choose an artificial intelligence model as the base model; The base model is trained on the training set, and the learning rate and other hyperparameters are adjusted on the validation set to obtain the pre-trained model. By validating the pre-trained model on the test set, a score analysis model is finally obtained, which takes penalty analysis data as input and outputs penalty scores.
8. The e-government data access control method according to claim 1, characterized in that, The dynamic updating of the access list based on access records includes: Extract the access records and perform structured operations on them to obtain the access structure records; The hash value H1 is obtained by performing a hash function on the access structure record. A transaction is generated by calling a specific method of the smart contract on the linked list; the specific method of the smart contract is the addRecord(H1) method; Valid transactions that have undergone transaction distribution and consensus verification are packaged into a new block; When a new block is successfully added to the access list, the smart contract generates a transaction receipt to dynamically update the access list; the transaction receipt includes the transaction hash, block hash, and block height.
9. An e-government data access control system, characterized in that, include: A data acquisition module and a data analysis module; the data acquisition module and the data analysis module are connected to each other; The data acquisition module acquires government data and access data through data acquisition equipment; the government data refers to the data that users need to access; the access data refers to the relevant data generated when users access data. The data analysis module includes a decision generation unit, a record generation unit, and a linked list update unit; The decision generation unit generates authorization decisions based on access data; The record generation unit generates access records based on government data and authorization decisions. The linked list update unit dynamically updates the access linked list based on the access records.
10. A computer-readable storage medium applied to the e-government data access control system of claim 9, characterized in that, The computer program is stored on the computer-readable storage medium.