Research On Graph-Structured Data Privacy Protection For Social Network Application

With the rapid development of Internet and mobile communication technologies,the role of social networks in production and life is becoming increasingly important.Users share information and expand their contact by means of network.Customers can be influenced directly by enterprises on the social network.Analyzing social network can simulate and predict group behaviors and group future trends.And analyzing social network can also help enterprise get decision support from social network for making user promotion and developing economic,However,third party directly uses published social network for analyzing will inevitably reveal individual sensitive information in the social network.It is necessary to trade off the privacy and utility while sharing and releasing social network.In recent years,privacy-preserving social network publishing has become one of the hot spots.The main work of the paper is as follows:(1)Existing protection has disadvantage of weak privacy protection and poor effect of node r eachability,Concerning these problems,a nove strategy for evaluating loss of node reachability is proposed.And base on the k~2-anonymous privacy model,devising an anonymous publishing algorit hm(RFA)for social networks to retain reachability.RFA can reduce loss of reachability between nodes and improve protected effect of social network.(2)To cope with the problem that the existing privacy protection deficiency that has a weak effect on maintaining the social network node structure similarity,a measurement method for reducing loss of node structure similarity is proposed.Based on the proposed method,designing social network anonymous publishing algorithm SPA that satisfies k-degree anonymity privacy model while reducing the loss of node structure similarity.The key idea of SPA is grouping node,which can protect the privacy of individual nodes while maintaining node structurel similarity.Theoretical analysis and experimental results show that the proposed social network protection can ensure the security of social network sensitive information while retaining the utility of social network.