Authority control method based on social networking group

Abstract

The invention relates to an authority control method based on social networking group, comprising: dividing all nodes of an original image representing SNS (social networking services) into a sub-image G1 containing a liberal point group and a sub-image G2 containing a conservative point group; performing arbitrary operations to the sub-images according to the characteristic values and the characteristic vectors and outputting the images after the arbitrary operations; determining the authority of the images after arbitrary operations and outputting the accessing information required by certain node. The method is designed according to the relation type data privacy protection and the requirement on the practicability in a social networking; and the edge randomization algorithm based on the social networking group not only increases the privacy protection level of the social networking but also ensures the practicability of the social networking structure to realize both protection and sharing of relation data.

Description

technical field [0001] The invention relates to a social network privacy protection technology, and is a social network group-based authority control method. Background technique [0002] Social Networking (SNS) is a complex network whose data features include nonlinearity, large capacity, scale-free, and high-dimensionality. Due to the needs of scientific research and data application sharing, social network data is collected and released in large quantities, which also leads to the problem of abuse of private information. Therefore, how to realize safe and efficient privacy data protection and access control is one of the important issues that SNS needs to solve. [0003] Among the existing privacy protection mechanisms, the most basic schemes will have attribute-based privacy protection. However, with the development of SNS attack technology, it is not enough to only protect node attributes. For example, the published social relationship structure also contains a large ...

AI Technical Summary

Problems solved by technology

Since the objects of randomization are all nodes and all edges in the graph, the amount of data is very large. In addition, the topological structure of the new network is different from that of the original network before and after randomization. Although the purpose of protecting the privacy of the social network is achieved, the processing of the new network will reduce the availability of original data

[0005] The feature saliency of the graph is an important parameter to measure the usability of the graph, but this characteristic is not fully considered in the general randomization perturbation technology

Considering that social network, as a virtual communication medium, is a platform for sharing information and keeping in touch with others, it is obvious that traditional encryption technology cannot meet users' needs for data protection and sharing

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