Research On Preserving Privacy In Social Networks Against Connection Fingerprint Attacks

With the advent of the Internet and the age of big data,the Internet has brought great convenience to people,but also makes people's privacy protection threaten.Because under the existing network environment,it is easier to collect,integrate,analyze and disseminate user's information than before,and it is more easily to make user's privacy information leak.So how to protect personal privacy on the Internet has become a hot issue.At present,there are many methods and models on social network privacy protection,and k-anonymity is the most classic social network privacy protection algorithm.It requires that every marked record have at least k-1 same records in the anonymous data set.Thus,k-anonymity social network privacy protection algorithm protects privacy in a certain sense.However,the existing k-anonymous technology,which make the assumption that all social network nodes are private and ignore that the actual network has a large number of public nodes.These public nodes identities are public,and attackers can exploit their connection with private nodes as a background to re-identify privacy attacks on private nodes,which is Connection Fingerprint(CFP)attacks.The existing algorithm against CFP attacks protects the centrality utility of public users,but there are still shortcomings,which is not to consider the nature of social networking charts as much as possible.On this basis,this paper presents an improved social network privacy protection algorithm against CFP attacks.The main work of this paper is as follows.First,we analyses the existing algorithm about preserving privacy in social networks against CFP attacks.In the process of edge replacement,the nodes in the same equivalent group are selected randomly,which ignores the centrality of each private node in the network graph.Second,aiming at the shortcomings of the existing algorithm about preservingprivacy in social networks against CFP attacks,we propose an improved algorithm about preserving privacy in social networks against CFP attacks,k-anonymity algorithm for n-hops CFP attacks.The idea is that to every private node v,in the range of n hops,there are at least the other k-1 nodes with the same public nodes.Considering some evaluation criteria of the nature of the social network graph,we select the network clustering coefficient as theoretical basis to process the edge replacement,and code the two algorithms.Finally,the comparison between the original algorithm and the improved algorithm is carried out on the selected data set,which are email-Eu-core,College Msg,Facebook and ca-Gr Qc.Through the comparison experiment,it can be found that in the case of the same time performance,the improved algorithm is more able to protect the centrality of nodes to some extent,especially the closeness centrality and betweenness centrality;On the network clustering coefficient,the improved algorithm has better effect than the former.