Privacy protecting method for social network based on undirected graph modification

Abstract

The invention discloses a privacy protecting method for a social network based on undirected graph modification. The method comprises the following steps: (1) establishing an undirected graph of the social network; (2) digging the maximum frequent subgraph; (3) establishing a node mapping relation; (4) iteratively expanding the subgraph and improving the mapping table; and (6) forming an anonymity isomorphic graph by adding mapping lines and virtual nodes to the undirected graph. According to the method, the virtual nodes are added, so that each node has other nodes symmetrical thereto, so that re-positioning of user identity is avoided, and personal privacy safety of the user is effectively protected. In addition, the local structure of the social network is finely modified, social network graph data can be externally and safely issued, so that the method is suitable for researching characteristic analysis and statistics of the local structure of the social network, and promotes data digging technology in research and application in the field of social network.

Description

technical field [0001] The invention belongs to the technical field of network privacy encryption, and in particular relates to a social network privacy protection method based on undirected graph modification. Background technique [0002] A social network refers to an Internet application that provides communication and interaction services for online aggregated users in various forms based on certain social relationships or common interests. This kind of social relationship network established with the relationship between people as the core is mapped on the Internet to form a user-centered and people-oriented Internet application. There is a certain correspondence between the connections between nodes on the social network and the connections between offline people, so mining the local structural information of the social network has great economic and social value. With the popularization and promotion of social networks such as Facebook and Twitter, people are increas...

AI Technical Summary

Problems solved by technology

Although these methods prevent simple privacy attacks, due to the complexity of data, the diversity of attack models, and different data application scenarios in social networks, the attack methods to be faced will be more complex.

When the attacker collects the user's multi-level neighbor relationship and searches for the target user in the published social network graph, neither of the previous two methods can effectively prevent this complex attack method

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