A fine-grained access control method for massive graph data based on security labels

A technology of security labeling and access control, applied in the field of massive data processing, which can solve problems such as capacity and diversity characteristics that exacerbate security and privacy issues.

Active Publication Date: 2021-09-10
BEIJING SCISTOR TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

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.

Method used

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  • A fine-grained access control method for massive graph data based on security labels
  • A fine-grained access control method for massive graph data based on security labels
  • A fine-grained access control method for massive graph data based on security labels

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Embodiment Construction

[0024] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0025] The present invention proposes a fine-grained access control method for massive graph data based on security labels, which is a special design and access control optimization for graph data stored in key-value type. In key-value stored graph data, both nodes and edges are part of the graph data. When organizing graph data by points, such as figure 1 As shown, the point is used as the key KEY, the attribute of the point and the edge associated with the point are used as the value VALUE, and for the edge, the edge is used as the key, and the attribute of the edge is used as the value. The invention sets security labels on key-value pairs of points or edges for access control of points or edges, and sets security labe...

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Abstract

The invention provides a fine-grained access control method for massive image data based on a security label, which belongs to the field of massive data processing. The method of the present invention includes: adding a security label to the point / edge / attribute storage structure and user information of the graph data to determine the security requirements required for access. The security label does not need to be defined in advance and is set after data generation or security design is completed ; When querying graph data, compare the user security label with the security label of the vertex / edge / attribute data to determine whether the user has permission to access the data. The security label of the present invention supports logical expression representation, adopts point, edge, and attribute three-level granularity security labels, and realizes security access control of different granularities; the present invention also uses bit operations to accelerate security label logic calculation and point / edge / attribute data filter calculation. The method of the invention greatly improves the security of mass image data access without sacrificing the graph data access performance.

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...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F21/62G06F16/583G06F16/538G06F16/51
Inventor 谢铭李晓军冯加军陈锡伟李晓静陈龙
Owner BEIJING SCISTOR TECH
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