Data privacy protection-oriented use control method for data standard semantic transformation

Abstract

The invention discloses a data privacy protection-oriented use control method for standard semantic conversion of data, relates to the technical field of standard semantic conversion and dynamic use control in a data privacy protection environment, and aims to solve the problems of data privacy protection and use control in a multi-party data sharing scene. The privacy and security of the data in the sharing process are ensured, and meanwhile, the requirements of laws and regulations such as GDPR on data sovereignty and use transparency are met. Comprising the following steps: S1, data desensitization processing; s2, encrypted storage and transmission; s3, applying a privacy computing technology; s4, authority control and access management; s5, data standard semantic conversion; s6, laws and regulations are followed and risks are evaluated; the S1 comprises privacy semantic model construction, data privacy related ontology and semantic rule definition, and adopts but not limited to RDF and OWL semantic technology standards, the S2 comprises dynamic use control, and strategy-based dynamic use control is realized by combining ODRL and SHACL, and the S3 comprises privacy compliance verification.

Description

Technical Field

[0001] The present invention relates to the technical field of standard semantic transformation and dynamic use control in a data privacy protection environment, and in particular to a use control method for data standard semantic transformation oriented to data privacy protection. Background Art

[0002] Data semanticization is a way to transform data into something readable and understandable so that people can better understand and analyze the data;

[0003] The problem of data privacy protection and usage control in multi-party data sharing scenarios is to ensure the privacy and security of data during the sharing process, while meeting the requirements of GDPR and other regulations on data sovereignty and usage transparency; therefore, the market urgently needs to develop a usage control method for data standard semantic transformation for data privacy protection to help people solve existing problems. Summary of the invention

[0004] The purpose of the present invention is to provide a usage control method for data standard semantic transformation for data privacy protection, so as to solve the data privacy protection and usage control problems in the multi-party data sharing scenario proposed in the above background technology, ensure the privacy and security of data in the sharing process, and meet the requirements of GDPR and other regulations on data sovereignty and usage transparency.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for controlling the use of data standard semantic transformation for data privacy protection, comprising the following steps:

[0006] S1: Data desensitization;

[0007] S2: encrypted storage and transmission;

[0008] S3: Application of privacy computing technology;

[0009] S4: Permission control and access management;

[0010] S5: Data standard semantic transformation;

[0011] S6: Regulatory compliance and risk assessment;

[0012] The S1 includes the construction of a privacy semantic model, which defines the ontology and semantic rules related to data privacy, and adopts semantic technology standards including but not limited to RDF and OWL. The S2 includes dynamic usage control, which combines ODRL and SHACL to implement policy-based dynamic usage control. The S3 includes privacy compliance verification, which adopts SHACL and SPARQL to verify and query data privacy policy.

[0013] The above technical solutions can realize standard semantic transformation and dynamic usage control in a data privacy protection environment. By combining data sovereignty with semantic web technology, strong privacy protection and compliance support are provided for data sharing and use. The system adopts semantic technology standards such as RDF and OWL, combined with SHACL to define data privacy policies, and at the same time, strict control of data access is achieved through encryption technology and distributed identity management. Using smart contracts and dynamic usage policies, access, use and permission changes in the data sharing process are tracked and verified in real time, thereby ensuring the sovereignty of the data provider and the transparency of the use process.

[0014] In a preferred example, the present invention can be further configured as follows: S4 includes distributed identity and access management, and identity and access rights management is performed through a distributed system; S6 includes smart contracts and auditing, and blockchain technology is used to achieve traceability and auditing of the data usage process.

[0015] In a preferred example, the present invention can be further configured as follows: S1 includes but is not limited to character replacement, hash encryption, and data randomization, which processes sensitive information in the original data so that it cannot be directly identified, thereby protecting personal privacy.

[0016] In a preferred example, the present invention can be further configured as follows: S2 includes but is not limited to AES, RSA, and DES, and encrypts and stores sensitive data to ensure the security of the data during storage and transmission.

[0017] In a preferred example, the present invention can be further configured as follows: S3 includes but is not limited to secure multi-party computing based on protocol rules, federated learning based on modern cryptography, and trusted execution environment technology based on hardware closed loop, which performs data calculation and analysis without leaking the original data, thereby protecting data privacy.

[0018] In a preferred example, the present invention can be further configured as follows: S4 includes role-based access control (RBAC) and attribute-based access control (ABAC), and dynamically controls user access rights to data by defining permissions or access policies for different roles, thereby protecting data privacy.

[0019] In a preferred example, the present invention can be further configured as follows: in S5, the semantics of the data is analyzed and transformed by utilizing natural language processing and machine learning techniques to ensure that the data can be accurately and effectively understood and used while protecting privacy.

[0020] In a preferred example, the present invention can be further configured as follows: in the entire data privacy protection process in S6, the GDPR-related regulatory requirements must be strictly followed. At the same time, a privacy risk assessment is required to identify and reduce the privacy risks faced when processing personal information.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] This invention realizes standard semantic transformation and dynamic usage control in a data privacy protection environment. By combining data sovereignty with semantic web technology, it provides strong privacy protection and compliance support for data during sharing and use. The system adopts semantic technology standards such as RDF and OWL, combined with SHACL to define data privacy policies, and implements strict control of data access through encryption technology and distributed identity management. Using smart contracts and dynamic usage policies, access, use and permission changes during data sharing are tracked and verified in real time, thereby ensuring the sovereignty of data providers over their data and the transparency of the usage process. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flow chart of a method for controlling the use of data standard semantic transformation for data privacy protection according to the present invention; DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0025] An embodiment provided by the present invention: Referring to Figure 1 , a method for controlling the use of data standard semantic transformation for data privacy protection, comprising the following steps:

[0026] S1: Data desensitization;

[0027] S2: encrypted storage and transmission;

[0028] S3: Application of privacy computing technology;

[0029] S4: Permission control and access management;

[0030] S5: Data standard semantic transformation;

[0031] S6: Regulatory compliance and risk assessment;

[0032] S1 includes the construction of a privacy semantic model, which defines the ontology and semantic rules related to data privacy, and adopts semantic technology standards including but not limited to RDF and OWL. S2 includes dynamic usage control, which combines ODRL and SHACL to implement policy-based dynamic usage control. S3 includes privacy compliance verification, which uses SHACL and SPARQL to verify and query data privacy policies.

[0033] S4 includes distributed identity and access management, which manages identity and access rights through a distributed system. S6 includes smart contracts and auditing, which uses blockchain technology to achieve traceability and auditing of the data usage process.

[0034] S1 includes but is not limited to character replacement, hash encryption, and data randomization, which processes sensitive information in the original data so that it cannot be directly identified, thereby protecting personal privacy.

[0035] S2 includes but is not limited to AES, RSA, and DES, which encrypt and store sensitive data to ensure the security of data during storage and transmission.

[0036] S3 includes but is not limited to secure multi-party computing based on protocol rules, federated learning based on modern cryptography, and trusted execution environment technology based on hardware closed loop. It performs data calculation and analysis without leaking the original data, thereby protecting data privacy.

[0037] S4 includes role-based access control (RBAC) and attribute-based access control (ABAC), which dynamically controls user access rights to data by defining permissions or access policies for different roles, thereby protecting data privacy.

[0038] S5 uses natural language processing and machine learning technologies to analyze and transform the semantics of data, ensuring that data can be understood and used accurately and effectively while protecting privacy.

[0039] In S6, the GDPR-related regulatory requirements must be strictly followed throughout the entire data privacy protection process. At the same time, a privacy risk assessment is required to identify and reduce the privacy risks faced when processing personal information.

[0040] Working principle: First, clarify the goals and principles of data privacy protection, determine the type of data that needs to be protected, the degree of sensitivity, and the specific requirements for privacy protection, and comply with relevant laws and regulations, such as GDPR, to ensure the legality and compliance of data processing activities1. Secondly, classify and grade data, comprehensively sort out the data, classify it according to its sensitivity and importance, set corresponding security levels and access rights for different categories of data, and ensure data hierarchical management2. Then, select and apply privacy protection technology. According to the results of data classification and grading, select appropriate privacy protection technology, such as data desensitization, encryption technology, privacy computing, etc. Data desensitization can delete or replace sensitive information in the data to make it unrecognizable; encryption technology can encrypt data to ensure the security of data during transmission and storage; privacy computing technology can analyze and process data without exposing the original data13. Then, design and implement access control strategies and formulate strict Access control mechanisms should be established to limit access rights to data. Identity verification, permission management and other means should be used to ensure that only authorized users can access sensitive data. Audit and monitoring mechanisms should be established to record data access and usage, and abnormal behavior should be discovered and handled in a timely manner. 2. In addition, the construction of data security management systems should be strengthened, a sound data security management system should be formulated, data security responsibilities should be clarified, data security training should be strengthened, employees' data security awareness and skills should be enhanced, data security risk assessments and audits should be conducted regularly, and security risks should be discovered and rectified in a timely manner. 5. At the same time, attention should be paid to the dynamic development of data privacy protection, the development trend of privacy protection technology and changes in laws and regulations should be paid close attention, and privacy protection measures should be updated and adjusted in a timely manner to adapt to new technical environments and legal requirements. 6. Finally, an emergency response mechanism should be established, and emergency response plans for security incidents such as data leaks should be formulated. Once a security incident occurs, the emergency response mechanism should be immediately activated, measures should be taken quickly to control the development of the situation, and it should be reported and handled in accordance with regulations.

[0041] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for controlling the use of data standard semantic transformation for data privacy protection, characterized in that: The steps include: S1: Data desensitization; S2: encrypted storage and transmission; S3: Application of privacy computing technology; S4: Permission control and access management; S5: Data standard semantic transformation; S6: Regulatory compliance and risk assessment; The S1 includes the construction of a privacy semantic model, which defines the ontology and semantic rules related to data privacy, and adopts semantic technology standards including but not limited to RDF and OWL. The S2 includes dynamic usage control, which combines ODRL and SHACL to implement policy-based dynamic usage control. The S3 includes privacy compliance verification, which adopts SHACL and SPARQL to verify and query data privacy policy.

2. The method for controlling the use of data standard semantic transformation for data privacy protection according to claim 1, characterized in that: The S4 includes distributed identity and access management, which manages identity and access rights through a distributed system. The S6 includes smart contracts and auditing, which uses blockchain technology to achieve traceability and auditing of the data usage process.

3. The method for controlling the use of data standard semantic transformation for data privacy protection according to claim 1, characterized in that: The S1 includes but is not limited to character replacement, hash encryption, and data randomization, which processes sensitive information in the original data so that it cannot be directly identified, thereby protecting personal privacy.

4. The method for controlling the use of data standard semantic transformation for data privacy protection according to claim 1, characterized in that: The S2 includes but is not limited to AES, RSA, and DES, which encrypt and store sensitive data to ensure the security of data during storage and transmission.

5. The method for controlling the use of data standard semantic transformation for data privacy protection according to claim 1, characterized in that: The S3 includes but is not limited to secure multi-party computing based on protocol rules, federated learning based on modern cryptography, and trusted execution environment technology based on hardware closed loop, which can perform data calculation and analysis without leaking the original data, thereby protecting data privacy.

6. The method for controlling the use of data standard semantic transformation for data privacy protection according to claim 1, characterized in that: The S4 includes role-based access control (RBAC) and attribute-based access control (ABAC), which dynamically controls user access rights to data by defining permissions or access policies for different roles, thereby protecting data privacy.

7. The method for controlling the use of data standard semantic transformation for data privacy protection according to claim 1, characterized in that: The S5 uses natural language processing and machine learning technologies to analyze and transform the semantics of the data, ensuring that the data can be accurately and effectively understood and used while protecting privacy.

8. The method for controlling the use of data standard semantic transformation for data privacy protection according to claim 1, characterized in that: In the entire data privacy protection process in S6, the GDPR-related regulatory requirements must be strictly followed. At the same time, a privacy risk assessment is required to identify and reduce the privacy risks faced when processing personal information.

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