A Distributed Social Network Privacy Preservation Method for Maintaining Node Reachability

Abstract

The invention discloses a distributed social network privacy protection method capable of keeping node reachability. Based on a graph data disturbance thought and a characteristic that a distributed graph processing system GraphX takes a node as a center, random disturbance of all reachable nodes and edges in an r-hop range of the nodes is searched for through message transmission among the nodes and updating of node values. According to the method, while the defects of low processing efficiency and poor processing capability during large-scale graph data processing in the prior art are well overcome, the influence of a privacy protection technology on original graph data is better reduced and the data availability is improved.

Description

technical field [0001] The invention belongs to the field of privacy protection, in particular to a distributed social network privacy protection method for maintaining node accessibility. Background technique [0002] Online social networks provide users with a platform for information exchange and sharing, and have become an indispensable part of people's online life today. When users use online social networks, they generate a large amount of network data. Through data mining and analysis, many questions such as sociology and psychology can be answered. However, social network data contains a large amount of personal privacy information. If no protection measures are taken , will inevitably lead to the leakage of personal privacy. Therefore, research on privacy protection of social network data has become a research hotspot in the field of privacy protection. [0003] In the privacy protection technology of social network, the privacy protection technology based on the ...

AI Technical Summary

Problems solved by technology

However, current graph structure obfuscation techniques do not consider the impact of graph data obfuscation techniques on the reachability between nodes

With the development of Internet technology, social network data presents the characteristics of large data scale and data diversity, which causes the problem of low processing efficiency in traditional privacy protection methods when dealing with large data sets. Parallel computing to achieve social network privacy protection has been become the future trend

[0004] Most of the above methods for social network privacy protection only solve the problem of individual privacy leakage in social networks, ignoring the accessibility of nodes

In addition, the above privacy protection technology also has the problems of low processing efficiency and poor processing ability when dealing with large-scale social network graph data.

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