Mass graph data fine-grained access control method based on security tags

Abstract

The invention provides a mass graph data fine-grained access control method based on security tags, and belongs to the field of mass data processing. The method comprises the steps that a security label is added in a point / edge / attribute storage structure of graph data and user information and used for determining security requirements needed by access, the security label does not need to be defined in advance, and the security label is set after data generation or security design is completed; and when graph data query is carried out, the user security label is compared with the security label of the point / edge / attribute data to judge whether the user has permission to access the data or not. The security tag supports logic expression representation, and security access control of different granularities is realized by adopting a point, edge and attribute three-level granularity security tag; according to the invention, bit operation is adopted to accelerate security label logic calculation and filtering calculation of point / edge / attribute data. According to the method, the security of mass graph data access is greatly improved while the graph data access performance is not sacrificed.

Description

technical field [0001] The invention belongs to the technical field of mass data processing, and relates to a technical solution for performing fine-grained access control on mass key-value storage map data based on security labels. Background technique [0002] The development of information and communication technology has opened up the era of the Internet of Everything. With the help of graph data processing technology in big data, the potential value of a large amount of related data in social, e-commerce, transportation, finance, Internet of Things and other industries can be tapped. Compared with traditional relational data, graph data is especially suitable for large-scale relationship analysis. It has the following characteristics: First, graph data adopts entity relationship modeling that is closer to the real world, which is intuitive and easy to understand, while relational data is more complex and abstract. Second, graph data query uses a way that is more suitabl...

AI Technical Summary

Problems solved by technology

However, while graph data processing improves the analysis effect of massive relational data, the inherent speed, capacity and diversity of big data also exacerbate security and privacy issues.

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