Grading use method based on sensitive data

Abstract

The invention discloses a hierarchical use method based on sensitive data, and belongs to the technical field of data information security, and the method comprises the steps: basic data collection: obtaining overall data information which needs to be used by a demand side to the inside or the outside; identifying and preprocessing data; sensitive data storage processing: for sensitive data, storing the sensitive data in a private cloud or configuring a secret-related server; a data sensitivity evaluation model, which is used for formulating a sensitive data evaluation model according to the use route of the sensitive data through different policies; sensitive data classification processing: performing label processing on the data according to a result output by model evaluation, and providing corresponding limitation when the data is provided outwards; grading use of sensitive data; sensitive data use logs; a centralized management and control platform is used for data. According to the invention, through intelligent classification, authority control and dynamic adjustment of the access strategy, the security and compliance of the sensitive information in different use scenes are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of data information security, and in particular to a hierarchical use method based on sensitive data. Background Art

[0002] With the rapid development of information technology, enterprises and institutions have accumulated a large amount of data containing sensitive information such as personal privacy and business secrets in their daily operations. Traditional data protection methods often adopt a "one-size-fits-all" encryption and access control strategy, which is not only inefficient, but may also affect the flexibility of business processes due to excessive restrictions. Therefore, there is an urgent need for a method that can flexibly adjust usage permissions according to the sensitivity of data to balance the needs of data security and business efficiency. Summary of the invention

[0003] The technical task of the present invention is to address the above shortcomings and provide a hierarchical usage method based on sensitive data, which ensures the security and compliance of sensitive information in different usage scenarios through intelligent classification, permission control and dynamic adjustment of access strategies.

[0004] The technical solution adopted by the present invention to solve its technical problem is:

[0005] A hierarchical use method based on sensitive data, the implementation of the method includes:

[0006] Basic data collection, obtaining overall data information that needs to be provided internally or externally to the demand side;

[0007] Data identification and preprocessing;

[0008] Sensitive data storage and processing: Sensitive data needs to be stored in a private cloud or configured with a confidential server;

[0009] Data sensitivity assessment model: through different policies, according to the usage route of sensitive data, a sensitive data assessment model is formulated;

[0010] Sensitive data is graded and handled. For the output of model evaluation, the data is labeled and provided with corresponding restrictions when it is provided externally;

[0011] Sensitive data is used in a graded manner;

[0012] Sensitive data usage logs: retain logs of usage information provided to the outside world. When data is leaked later, analyze it in combination with the previous sensitive data usage logs, and feed the results back to the model to further optimize the model.

[0013] Data usage is centralized on the control platform.

[0014] Furthermore, the basic data collection includes personal identity and authentication information, network identity authentication data, service content and data management, user service usage records, equipment-related information, user behavior analysis data, network construction and planning data, network and system resource data, network operation and maintenance and management system data, network security management data, business operation information, public business operation information, company development strategy information, business expansion data, technology research and development data, operation management data, production and operation statistics, comprehensive management data and partner shared data.

[0015] Furthermore, the data identification and preprocessing include:

[0016] Clean the collected data and remove duplicate and invalid data;

[0017] According to the rules, sensitive data and non-sensitive data are distinguished. This involves different rules of different companies. Non-sensitive data is retained.

[0018] Furthermore, the data sensitivity assessment model includes:

[0019] Extract sensitive data and evaluate it from policy and regulatory compliance models, risk assessment models, context-related models, and data leakage history

[0020] Based on the evaluation results, the four-level classification model is combined to classify the sensitive data as a whole; the four-level classification model, combined with the sensitive data standard, classifies the sensitive data into four levels: extremely sensitive, sensitive, medium sensitive and low sensitive;

[0021] The entire life cycle of stored data is managed through the data lifecycle management model, and sensitive and non-sensitive data are re-labeled when the data has been desensitized or discarded.

[0022] Furthermore, the policy and regulatory compliance model includes:

[0023] 1.1) Identify applicable regulations;

[0024] 1.2) Data classification and labeling: classify data according to the requirements of policies and regulations, and identify sensitive data that is protected by specific regulations;

[0025] 1.3) Establish a compliance strategy and formulate corresponding data protection policies based on the identified regulatory requirements;

[0026] 1.4) Monitoring and auditing: Establish a monitoring mechanism to regularly check whether data processing activities comply with internal policies and external regulatory requirements;

[0027] 1.5) Continuous improvement: Establish a continuous improvement process, regularly review and update data protection measures to ensure consistency with the latest regulatory requirements;

[0028] The risk assessment model includes:

[0029] 2.1) Data identification and mapping: Conduct a comprehensive data asset inventory to identify all data stored, processed, and transmitted in the organization, including drawing data flow diagrams to clarify the source, storage location, usage, and final destination of the data;

[0030] 2.2) Sensitivity Assessment: Assess the sensitivity of the data based on its nature and content; generally, personally identifiable information, financial data, health records, intellectual property, etc. are considered highly sensitive.

[0031] 2.3) Risk Analysis: Conduct risk analysis on each data item, taking into account its sensitivity and identified threats; assess the probability of data leakage and the extent of damage that may be caused if a leakage occurs.

[0032] 2.4) Continuous monitoring and review: The risk environment and data processing practices are constantly changing, so risks need to be reassessed regularly and data classification and protection measures need to be adjusted based on new threats or compliance requirements;

[0033] Context-sensitive models include:

[0034] 3.1) Contextual understanding: Analyze the text, metadata, time, location and other information surrounding the data to understand the specific context in which the data is used;

[0035] 3.2) Relationship network: build the relationship between data, including whether the data is related to other known sensitive information, or whether the data plays a key role in a specific business process;

[0036] Data breach history includes:

[0037] Obtain the history of previous data leaks and extract and evaluate the losses caused by the leaks.

[0038] Furthermore, the sensitive data is handled in a hierarchical manner, and different usage processes are set according to different sensitivity levels:

[0039] Extremely sensitive: cannot be provided externally;

[0040] Sensitive: provided to departments with certain authority;

[0041] Medium sensitive: for internal use after application;

[0042] Low sensitivity: can be provided to the public upon application;

[0043] The grade classification is shown in Table 1:

[0044] Table 1 - Model evaluation level classification table

[0045]

[0046]

[0047] Extremely sensitive: When the policy and regulatory compliance is A, all data is extremely sensitive and cannot be disclosed. Other extremely sensitive data are when there are more than two A in the remaining three models, AAA, AAB, and ABA.

[0048] Sensitive: When the policy and regulation compliance is B, the sensitive classification is that the remaining models have more than one A, such as ABB, ABC, ACC, etc.;

[0049] Medium sensitivity: When the policy and regulatory compliance is B, the medium sensitivity classification is that the remaining model contains more than 2 Bs, such as BBC, BBB, etc.

[0050] Low sensitivity: When the policy and regulatory compliance is B, the low sensitivity classification is the remaining model that does not include A and more than two Bs, such as BCC, CCC, etc.

[0051] Furthermore, logs of usage provided to the outside are retained. When data is leaked in the later stage, the retained logs are combined with previous sensitive data for analysis, and the results are fed back into the model to further optimize the model.

[0052] Furthermore, the specific implementation of this method is as follows:

[0053] S1. Basic data collection and identification: Collect the raw data to be processed, which comes from the company's internal database, external partners or public sources; first, use regular expressions, keyword matching or machine learning algorithms to automatically identify sensitive data, such as personal identity information, financial records, medical health data, etc.

[0054] S2. Data preprocessing and cleaning: Preprocess the identified sensitive data, including desensitization (such as data obfuscation, anonymization, generalization) and data de-identification, to ensure the compliance of the data in the subsequent processing stage; at the same time, perform quality checks on non-sensitive data to eliminate redundancy and outliers;

[0055] S3, Sensitive data storage and access control: Different levels of encryption storage strategies are adopted according to the sensitivity level of the data; extremely sensitive data is stored in an environment with higher encryption strength, such as hardware security module (HSM), and physical access is restricted; medium and low sensitivity data is stored using software encryption; at the same time, fine-grained access control strategies are set to ensure that only authorized users or applications can access data of the corresponding level;

[0056] S4. Dynamic sensitivity assessment model: Use AI algorithms to build a dynamic data sensitivity assessment model. The model dynamically adjusts the sensitivity level based on data type, usage scenario, user role, and time factors. For example, in an emergency, the model may temporarily increase the access rights to sensitive data to cope with the crisis.

[0057] S5. Data usage authorization and tracking: Through role-based RBAC (Role-Based Access Control) or PBAC (Policy-Based Access Control) strategies, hierarchical authorization of data is achieved; when users access, the system will review and record access logs in real time, including time, operator, operation content, results, etc.;

[0058] S6. Intelligent monitoring and response: Integrate intelligent monitoring systems to analyze data usage logs and system behaviors in real time, detect abnormal access patterns, abuse of permissions, or signs of data leakage, and immediately trigger alarms and initiate response mechanisms, such as automatically restricting access and notifying the security team.

[0059] S7. Continuous optimization and feedback: Periodically feed back monitoring and usage log data to the sensitivity assessment model to optimize model accuracy and predictive capabilities; adjust data processing strategies based on actual usage feedback to ensure model adaptability and continuously improve the security and efficiency of data processing.

[0060] The present invention also claims a device for implementing hierarchical use based on sensitive data, comprising: at least one memory and at least one processor;

[0061] The at least one memory is used to store a machine-readable program;

[0062] The at least one processor is used to call the machine-readable program to implement the above method.

[0063] The present invention also claims protection for a computer-readable medium having computer instructions stored thereon, which implement the above method when executed by a processor.

[0064] Compared with the prior art, the hierarchical use method based on sensitive data of the present invention has the following beneficial effects:

[0065] This method can ensure the security and compliance of sensitive information in different usage scenarios through intelligent classification, permission control and dynamic adjustment of access strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 is a flowchart of a hierarchical use method based on sensitive data provided by an embodiment of the present invention;

[0067] Figure 2 is a diagram of a sensitive data assessment model provided by an embodiment of the present invention;

[0068] Figure 3 is a diagram of a four-level hierarchical model provided by an embodiment of the present invention;

[0069] Figure 4 It is a diagram showing the page effect of the hierarchical usage method based on sensitive data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0070] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0071] A hierarchical use method based on sensitive data, the implementation of the method includes:

[0072] S1. Basic data collection;

[0073] S2, data identification and preprocessing;

[0074] S3, sensitive data storage and processing;

[0075] S4, data sensitivity assessment model;

[0076] S5. Classification and disposal of sensitive data;

[0077] S6. Classified use of sensitive data;

[0078] S7, sensitive data usage log;

[0079] S8. Data usage centralized control platform.

[0080] In step S1, the overall data information to be provided internally or externally to the demander is obtained, and the collection type includes the following information:

[0081] Personal identity and authentication information: Personal identity information, as the basic identification of the data subject, is the cornerstone of all personal data processing activities. Its accuracy directly affects the personalization of services, the effectiveness of security verification, and the protection of user privacy. Authentication information is the key to ensuring account security and preventing identity fraud, and is essential for maintaining the security of user account funds and information.

[0082] With the popularization of network services, the security of login passwords and related verification materials is directly related to the privacy and property safety of users, and is the first line of defense against network fraud and information leakage. Its importance lies in ensuring that users can safely access their personal accounts, while ensuring that data is encrypted during transmission and storage to avoid unauthorized access.

[0083] Service content and data management. Clarifying service content helps users understand their rights and obligations and improves service transparency, while data management ensures the accuracy and consistency of information. It is crucial to improving user experience, optimizing service processes, and ensuring compliance with data use. It is the basis for improving corporate service quality and user satisfaction.

[0084] User service usage records, such data is not only the basis for billing and customer service, but also an important resource for optimizing services, predicting user needs, and identifying potential risks. By analyzing user behavior, companies can provide personalized services more accurately, while monitoring and preventing violations and ensuring the health of the platform ecosystem.

[0085] Device-related information: Mastering terminal device information is crucial for optimizing user experience, ensuring service compatibility, and conducting device health management and security protection. Especially in the era of mobile Internet, device information helps to quickly locate and solve problems, thereby improving user satisfaction.

[0086] User behavior analysis data: User behavior analysis is the cornerstone of modern marketing and product design. It helps companies understand user preferences, optimize product functions, and develop precise marketing strategies, thereby improving market competitiveness and user loyalty. Through in-depth analysis, companies can more effectively guide product iteration and market orientation.

[0087] Network construction and planning data, network layout and planning data are the basis for ensuring the reliability of network services, and are directly related to the scalability, cost-effectiveness and flexibility of future development of services. Reasonable planning can effectively reduce operating costs, improve service quality and user experience, and reserve space for technological innovation.

[0088] Network and system resource data, effective resource management is the key to supporting business continuity, involving efficient use and optimal configuration of network, computing, storage and other infrastructure. This is not only related to service stability and response speed, but also an important part of cost control and resource elasticity, and is crucial to improving enterprise operational efficiency.

[0089] Network operation and management system data, operation and maintenance data is the "lifeline" for maintaining healthy network operation, involving the entire process from network configuration to security monitoring and fault response. Its importance lies in preventing system failures, quickly restoring services, and ensuring data security. It is the core element to ensure business continuity and improve user experience.

[0090] Network security management data, in the face of increasingly complex network threats, network security management data is the "radar" and "shield" of the enterprise security defense system. It covers comprehensive defense measures from security audits, emergency response to threat intelligence, ensuring that enterprises can detect and resist external attacks in a timely manner and protect the security of enterprise and user data.

[0091] Business operation data is the direct basis for corporate strategic decision-making, marketing, and customer service. Through accurate product positioning, efficient channel management, and high-quality customer service records, companies can continuously optimize business processes, enhance market competitiveness, and achieve sustainable development.

[0092] Public business operation information and transparent corporate information help build public trust, attract investors and partners, and are also an important part of corporate brand building. It demonstrates a company’s social responsibility, achievements, and future plans, and has an undeniable impact on the shaping of a company’s image and market perception.

[0093] Company development strategy information, a clear development strategy is the compass for the company to move forward, affecting key decisions such as resource allocation, technological innovation, and market expansion. It ensures that the company's goals are consistent and resources are used efficiently, and plays a decisive role in maintaining the company's competitiveness and achieving long-term development.

[0094] Business development data is a key reference for enterprises to explore new markets, develop new products, and optimize marketing strategies. It helps enterprises find opportunities in the fiercely competitive market, evaluate risks and returns, formulate more forward-looking and competitive market strategies, and promote the sustainable growth of enterprises.

[0095] Technology R&D data, technology R&D is the core driving force of corporate innovation, and protecting technical secrets and intellectual property rights is the key to maintaining competitive advantage. R&D data not only reflects the technological strength of the company, but also guides the future direction of technological development, and is of great significance to enhancing the differentiation of products and services and strengthening market barriers.

[0096] Operation management data: Good operation management is the cornerstone of daily business operations, involving process optimization, resource scheduling, efficiency improvement and other aspects. It ensures smooth internal operations, improves work efficiency, reduces costs, and provides solid operational support for the realization of the company's strategic goals.

[0097] Production and operation statistics are the yardstick for measuring the business conditions of enterprises and the basis for financial planning, performance evaluation, cost control and production planning. Accurate statistical analysis can help enterprises identify problems, grasp trends, provide scientific data support for decision-making, and promote the healthy development of enterprises.

[0098] Comprehensive management data, covering multiple dimensions such as human resources, financial management, and compliance, is the basis for ensuring compliance, efficiency, and coordination within the enterprise. Good comprehensive management can not only improve the level of corporate governance, but also enhance employee satisfaction and team cohesion, laying a solid foundation for the long-term and stable development of the enterprise.

[0099] Sharing data with partners: In an increasingly cooperative business environment, sharing data with partners has become the norm. Reasonable management of shared data can not only promote information exchange and collaborative work, but also ensure the security and compliance of data during the sharing process, maintain corporate reputation, deepen cooperative relationships, and jointly explore the market.

[0100] The data identification and preprocessing steps in step S2 are:

[0101] S2.1. Clean the collected data and remove duplicate and invalid data;

[0102] S2.2. According to the rules, distinguish between sensitive data and non-sensitive data. This involves different rules of different companies. Non-sensitive data should be retained.

[0103] The sensitive data storage processing step in step S3 is to store sensitive data in a private cloud or a designated confidential server.

[0104] The data sensitivity assessment model in step S4: through different policies, according to the usage route of sensitive data, a sensitive data assessment model is formulated, such as Figure 2 shown.

[0105] S4.1. Extract sensitive data and evaluate it from the policy and regulatory compliance model, risk assessment model, context-related model and data leakage history.

[0106] The policy and regulatory compliance model includes:

[0107] Identify applicable regulations: First, clarify the policies and regulations that apply to the scope of business and the type of data processed, including but not limited to the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), the Payment Card Industry Data Security Standard (PCI-DSS), etc.

[0108] Data classification and labeling: classify data according to the requirements of policies and regulations, and identify sensitive data (personal identity information, health records, financial transaction data, etc.) that are protected by specific regulations.

[0109] Establish compliance strategies and formulate corresponding data protection policies based on identified regulatory requirements; including data encryption, access control, data retention period, cross-border data transfer restrictions, security audit and monitoring mechanisms, etc.

[0110] Monitoring and Auditing: Establish a monitoring mechanism to regularly check whether data processing activities comply with internal policies and external regulatory requirements; conduct internal audits and consider third-party audits to verify compliance and promptly identify potential compliance risks.

[0111] Continuous improvement: Establish a continuous improvement process, regularly review and update data protection measures to ensure consistency with the latest regulatory requirements;

[0112] The assessment is completed at two levels.

[0113] The risk assessment model includes:

[0114] Data identification and mapping: Conduct a comprehensive data asset inventory to identify all data stored, processed, and transmitted in the organization, including drawing data flow diagrams to clarify the source, storage location, usage, and final destination of the data.

[0115] Sensitivity Assessment: Assess the sensitivity of the data based on its nature and content; typically, personally identifiable information, financial data, health records, intellectual property, etc. are considered highly sensitive.

[0116] Risk analysis: Conduct risk analysis on each data item, taking into account its sensitivity and identified threats; assess the probability of data leakage and the extent of damage that may be caused if a leakage occurs.

[0117] Continuous monitoring and review: The risk environment and data processing practices are constantly changing, so risks need to be reassessed regularly and data classification and protection measures need to be adjusted based on new threats or compliance requirements;

[0118] The context-dependent model includes:

[0119] Contextual understanding: Analyze the text, metadata, time, location and other information surrounding the data to understand the specific context in which the data is used.

[0120] Relationship network: Build connections between data, including whether the data is related to other known sensitive information or whether the data plays a key role in a specific business process.

[0121] The data breach history includes:

[0122] Obtain the history of previous data leaks and extract and evaluate the losses caused by the leaks.

[0123] The grade classification is shown in Table 1 below.

[0124] Table 1 - Model evaluation level classification table

[0125]

[0126] S4.2. Based on the evaluation results of S4.1, the sensitive data is classified as a whole in combination with a four-level classification model; the four-level classification model, combined with the sensitive data standard, classifies sensitive data into four levels: extremely sensitive, sensitive, medium sensitive and low sensitive, such as Figure 3 As shown,

[0127] Extremely sensitive: When the policy and regulatory compliance is A, all data is extremely sensitive and cannot be disclosed. Other extremely sensitive data are when there are more than two A in the remaining three models, AAA, AAB, and ABA.

[0128] Sensitive: When the policy and regulation compliance is B, the sensitive classification is that the remaining models have more than one A, such as ABB, ABC, ACC, etc.;

[0129] Medium sensitivity: When the policy and regulatory compliance is B, the medium sensitivity classification is that the remaining model contains more than 2 Bs, such as BBC, BBB, etc.

[0130] Low sensitivity: When the policy and regulatory compliance is B, the low sensitivity classification is the remaining model that does not include A and more than two Bs, such as BCC, CCC, etc.;

[0131] S4.3: Data lifecycle management model,

[0132] Manage the entire life cycle of stored data and re-label the data as sensitive or non-sensitive when it has been desensitized or discarded.

[0133] In step S5, sensitive data is handled in a hierarchical manner. For the output of the model evaluation, the data is labeled and corresponding restrictions are provided when it is provided externally.

[0134] The steps for using sensitive data in different levels are to set different usage processes according to different sensitivity levels:

[0135] Extremely sensitive: cannot be provided externally;

[0136] Sensitive: provided to departments with certain authority;

[0137] Medium sensitive: for internal use after application;

[0138] Low sensitivity: can be provided to the public upon application.

[0139] The sensitive data usage log in step S6 is as follows: retain the log of usage provided to the outside, and when data is leaked in the later stage, analyze it in combination with the previous sensitive data usage log retention, and feed the results back to the model.

[0140] The sensitive data usage log step in step S7 is as follows: retain the log of usage provided to the outside, and when data is leaked in the later stage, analyze it in combination with the previous sensitive data usage log retention, and feed the results back to the model to further optimize the model.

[0141] The embodiment of the present invention also provides a hierarchical use method based on sensitive data, and the specific implementation of the method is as follows:

[0142] S1. Basic data collection and identification: Collect the raw data to be processed, which comes from the company's internal database, external partners or public sources; first, use regular expressions, keyword matching or machine learning algorithms to automatically identify sensitive data, such as personal identity information, financial records, medical health data, etc.

[0143] S2. Data preprocessing and cleaning: Preprocess the identified sensitive data, including desensitization (such as data obfuscation, anonymization, generalization) and data de-identification, to ensure the compliance of the data in the subsequent processing stages; at the same time, perform quality checks on non-sensitive data to eliminate redundancy and outliers.

[0144] S3. Sensitive data storage and access control: Different levels of encryption storage strategies are adopted according to the sensitivity level of the data; extremely sensitive data is stored in an environment with higher encryption strength, such as a hardware security module (HSM), and physical access is restricted; medium and low sensitivity data is stored using software encryption; at the same time, fine-grained access control policies are set to ensure that only authorized users or applications can access data of the corresponding level.

[0145] S4. Dynamic sensitivity assessment model: Use AI algorithms to build a dynamic data sensitivity assessment model. The model dynamically adjusts the sensitivity level based on data type, usage scenario, user role, and time factors. For example, in an emergency, the model may temporarily increase access rights to sensitive data to respond to the crisis.

[0146] S5. Data usage authorization and tracking: Through role-based RBAC (Role-Based Access Control) or PBAC (Policy-Based Access Control) strategies, hierarchical authorization of data is achieved; when a user accesses data, the system reviews and records the access log in real time, including time, operator, operation content, results, etc.

[0147] S6. Intelligent Monitoring and Response: An integrated intelligent monitoring system analyzes data usage logs and system behaviors in real time to detect abnormal access patterns, abuse of permissions, or signs of data leakage. It immediately triggers alarms and initiates response mechanisms, such as automatically restricting access and notifying the security team.

[0148] S7. Continuous optimization and feedback: Periodically feed back monitoring and usage log data to the sensitivity assessment model to optimize model accuracy and predictive capabilities; adjust data processing strategies based on actual usage feedback to ensure model adaptability and continuously improve the security and efficiency of data processing.

[0149] The embodiment of the present invention also provides a device for implementing hierarchical use based on sensitive data, including: at least one memory and at least one processor;

[0150] The at least one memory is used to store a machine-readable program;

[0151] The at least one processor is used to call the machine-readable program to implement the hierarchical usage method based on sensitive data described in the above embodiment.

[0152] The embodiment of the present invention further provides a computer-readable medium, on which computer instructions are stored, and when the computer instructions are executed by a processor, the hierarchical use method based on sensitive data described in the above embodiment is implemented. Specifically, a system or device equipped with a storage medium can be provided, on which software program codes implementing the functions of any of the above embodiments are stored, and a computer (or CPU or MPU) of the system or device is enabled to read and execute the program code stored in the storage medium.

[0153] In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.

[0154] The storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer by a communication network.

[0155] In addition, it should be clear that the functions of any of the above embodiments can be implemented not only by executing the program code read by the computer, but also by enabling an operating system operating on the computer to complete part or all of the actual operations based on instructions from the program code.

[0156] In addition, it can be understood that the program code read from the storage medium is written to a memory provided in an expansion board inserted into the computer or written to a memory provided in an expansion unit connected to the computer, and then based on the instructions of the program code, a CPU installed on the expansion board or the expansion unit is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above-mentioned embodiments.

[0157] The present invention is shown and described in detail above through the accompanying drawings and preferred embodiments. However, the present invention is not limited to these disclosed embodiments. Based on the above multiple embodiments, those skilled in the art can know that the code review methods in the above different embodiments can be combined to obtain more embodiments of the present invention, and these embodiments are also within the protection scope of the present invention.

Claims

1. A hierarchical use method based on sensitive data, characterized in that: The implementation of this method includes: Basic data collection, obtaining overall data information that needs to be provided internally or externally to the demand side; Data identification and preprocessing; Sensitive data storage and processing: sensitive data is stored in a private cloud or configured on a confidential server; Data sensitivity assessment model: through different policies, according to the usage route of sensitive data, a sensitive data assessment model is formulated; Sensitive data is graded and handled. For the output of model evaluation, the data is labeled and provided with corresponding restrictions when it is provided externally; Sensitive data is used in a graded manner; Sensitive data usage logs: retain logs of usage information provided to the outside world. When data is leaked later, analyze it in combination with the previous sensitive data usage logs, and feed the results back to the model to further optimize the model. Data usage is centralized on the control platform.

2. A hierarchical use method based on sensitive data according to claim 1, characterized in that: The basic data collection includes personal identity and authentication information, network identity authentication data, service content and data management, user service usage records, equipment-related information, user behavior analysis data, network construction and planning data, network and system resource data, network operation and maintenance and management system data, network security management data, business operation information, public business operation information, company development strategy information, business expansion data, technology research and development data, operation management data, production and operation statistics, comprehensive management data and partner shared data.

3. The hierarchical use method based on sensitive data according to claim 1, characterized in that: The data identification and preprocessing include: Clean the collected data and remove duplicate and invalid data; According to the rules, sensitive data and non-sensitive data are distinguished, and non-sensitive data is retained.

4. The hierarchical use method based on sensitive data according to claim 1, characterized in that: The data sensitivity assessment model includes: Extract sensitive data and evaluate it from policy and regulatory compliance models, risk assessment models, context-related models, and data leakage history Based on the evaluation results, the four-level classification model is combined to classify the sensitive data as a whole; the four-level classification model, combined with the sensitive data standard, classifies the sensitive data into four levels: extremely sensitive, sensitive, medium sensitive and low sensitive; The entire life cycle of stored data is managed through the data lifecycle management model, and sensitive and non-sensitive data are re-labeled when the data has been desensitized or discarded.

5. A hierarchical use method based on sensitive data according to claim 4, characterized in that: The policy and regulatory compliance model includes: 1.1) Identify applicable regulations; 1.2) Data classification and labeling: classify data according to the requirements of policies and regulations, and identify sensitive data that is protected by specific regulations; 1.3) Establish a compliance strategy and formulate corresponding data protection policies based on the identified regulatory requirements; 1.4) Monitoring and auditing: Establish a monitoring mechanism to regularly check whether data processing activities comply with internal policies and external regulatory requirements; 1.5) Continuous improvement: Establish a continuous improvement process, regularly review and update data protection measures to ensure consistency with the latest regulatory requirements; The risk assessment model includes: 2.1) Data identification and mapping: Conduct a comprehensive data asset inventory to identify all data stored, processed, and transmitted in the organization, including drawing data flow diagrams to clarify the source, storage location, usage, and final destination of the data; 2.2) Sensitivity assessment: Assess the sensitivity of the data based on its nature and content; 2.3) Risk analysis: Conduct risk analysis on each data item, combining its sensitivity with the identified threats; 2.4) Continuous monitoring and review: Regularly reassess risks and adjust data classification and protection measures based on new threats or compliance requirements; Context-sensitive models include: 3.1) Contextual understanding: Analyze the text, metadata, time, and location information around the data to understand the specific context in which the data is used; 3.2) Relationship network: build the relationship between data, including whether the data is related to other known sensitive information, or whether the data plays a key role in a specific business process; Data breach history includes: Obtain the history of previous data leaks and extract and evaluate the losses caused by the leaks.

6. A hierarchical use method based on sensitive data according to claim 4, characterized in that: The sensitive data is handled in a graded manner, and different usage processes are set according to different sensitivity levels: Extremely sensitive: cannot be provided externally; Sensitive: provided to departments with certain authority; Medium sensitive: for internal use after application; Low sensitivity: can be provided to the public upon application; The grade classification is shown in Table 1: Table 1 - Model evaluation level classification table Extremely sensitive: When the policy and regulatory compliance is A, all data is extremely sensitive and cannot be made public. Other extremely sensitive data are when two or more of the remaining three models are A. Sensitive: When the policy and regulation compliance is B, the sensitive classification is that there is more than one A in the remaining models; Medium sensitivity: When the policy and regulatory compliance is B, the medium sensitivity classification is that the remaining model contains more than 2 Bs; Low sensitivity: When the policy and regulatory compliance is B, the low sensitivity classification is the remaining model that does not include A and more than two Bs.

7. The hierarchical use method based on sensitive data according to claim 1, characterized in that: The usage information provided to the outside is retained in logs. When data is leaked in the later stage, it is analyzed in combination with the previous sensitive data usage logs, and the results are fed back into the model to further optimize the model.

8. The hierarchical use method based on sensitive data according to claim 1, characterized in that: The specific implementation of this method is as follows: S1. Basic data collection and identification: Collect the raw data to be processed, which comes from the company's internal database, external partners or public sources; first, use regular expressions, keyword matching or machine learning algorithms to automatically identify sensitive data; S2. Data preprocessing and cleaning: Preprocess the identified sensitive data, including desensitization and data de-identification, to ensure the compliance of the data in the subsequent processing stage; At the same time, non-sensitive data is quality checked to eliminate redundancy and outliers; S3, Sensitive data storage and access control: Different levels of encryption storage strategies are adopted according to the sensitivity level of the data; extremely sensitive data is stored in an environment with higher encryption strength and physical access is restricted; medium and low sensitivity data is stored using software encryption; at the same time, fine-grained access control strategies are set to ensure that only authorized users or applications can access data of the corresponding level; S4. Dynamic sensitivity assessment model: Use AI algorithms to build a dynamic data sensitivity assessment model, which dynamically adjusts the sensitivity level based on data type, usage scenario, user role, and time factors; S5. Data usage authorization and tracking: Implement hierarchical authorization of data through role-based RBAC or PBAC policies; when users access data, the system audits and records access logs in real time; S6. Intelligent monitoring and response: Integrate intelligent monitoring systems to analyze data usage logs and system behaviors in real time, detect abnormal access patterns, abuse of permissions, or signs of data leakage, and immediately trigger alarms and initiate response mechanisms, such as automatically restricting access and notifying the security team. S7. Continuous optimization and feedback: Periodically feed back monitoring and usage log data to the sensitivity assessment model to optimize model accuracy and predictive capabilities; adjust data processing strategies based on actual usage feedback to ensure model adaptability and continuously improve the security and efficiency of data processing.

9. A device for implementing hierarchical use of sensitive data, characterized in that: include: at least one memory and at least one processor; The at least one memory is used to store a machine-readable program; The at least one processor is used to call the machine-readable program to implement the method described in any one of claims 1 to 8.

10. A computer-readable medium, characterized in that The computer readable medium stores computer instructions, and when the computer instructions are executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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