Research On Privacy Preservation In Social Network

With the rise of various social networking software,a lot of information has been accumulated in the social network.To a certain extent,this information reflects the law of society and forms a kind of data with important research significance and application value.Therefore,how to protect the privacy and security of users when mining the value of social network data is of great significance.Through the research and analysis of the current situation at home and abroad,the privacy protection algorithms of social network can be divided into clustering and graph perturbation based privacy protection methods.However,most of the existing privacy protection methods do not fully consider the combination of the characteristics of social network and clustering algorithm itself,resulting in unnecessary information loss and reducing the effectiveness of data.In order to better balance user privacy security and anonymous data validity,the following research is carried out on how to ensure user privacy security in data mining and analysis of social network:Firstly,the clustering-based privacy protection methods are studied and analyzed.It is found that most of the algorithms are based on subgraph k-anonymity model,which can not effectively resist attacks based on user attributes and subgraph structure as background knowledge.By studying the characteristics of social network and clustering,a clustering-based privacy protection method for social network is proposed,which combines clustering coefficients and node density to optimize the initial seed node algorithm to reduce the information loss in the anonymous process.An adaptive privacy protection strategy is designed to improve the security of anonymous data.The experimental results on different data sets show that the algorithm improves the security of anonymous data and ensures the validity of data.Secondly,for the weighted social network model,a clustering privacy protection algorithm based on node topology structure is proposed on the basis of clustering-based privacy protection algorithm.Using local topological structure of nodes,an anonymous social network model is obtained by clustering and generalizing weighted social network with greedy ideas.Because each clustering conforms to the principle of minimizing information loss,the data validity is guaranteed to the greatest extent while preventing attackers from relocating the target node according to the node structure.Finally,most existing algorithms neglect the influence of nodes on global structure,focusing only on the independence of nodes and ignoring the collective nature of nodes in social network.To solve this problem,a clustering privacy protection algorithm based on the global influence of nodes is proposed.According to the transmission characteristics of node signals in weighted social network,a weighted signal transmission model is designed.The weighted social network are modeled and the privacy protection algorithm is designed and implemented.The experimental results and algorithm analysis show that the algorithm is superior to other algorithms in security and data validity.