Research On Weighted Sequence And Shortest Path Privacy Protection Algorithm In Weighted Social Networks

With the changes of people's lifestyles and communication methods,the social network covers a large number of related information such as individuals and enterprises on the basis of information technology.Due to the analysis and application of various fields in scientific research and data mining of data in social networks will lead to the leakage of a large number of users' privacy.Among them,due to the weight of the weighted network and the leakage of users' privacy caused by the shortest path,it is gradually becoming the focus and difficulty of the research on privacy protection.The attacker will use the edge weight sequence to identify the node.At the same time,the existing methods for privacy protection of the shortest path have great changes to the network structure and cannot guarantee the validity of social network data.Based on the proposed problem,this paper proposes a more effective defense algorithm for the weighted sequence and the attribute attacks of the shortest path in weighted social networks.The main contents of this paper are as follows:1.Weighted privacy protection of existing weighted social networks is mainly based on the use of Gaussian multiplication and histogram anonymity methods to construct the network,lack of research on privacy leakage caused by the weighted sequence of edges,and insufficient consideration of data validity.Based on this,this paper will use the vector set to combine the distance between the inner diameter of the group and the relative distance of the nodes to achieve the privacy protection of the weight sequence.The algorithm can guarantee data validity of the data and resist the weight sequence attack.2.On the basis of k-anonymity,privacy protection is established by adding edges or deleting edges to construct k-shortest path.This method has a great influence on the structure of social networks,and the shortest path privacy level between each node pair only is k.It can only be said.Based on this,this article classifies all edges in the network into three categories: unvisited edges(7)NE(8),partially accessed edges(7)PE(8)and must-accessed edges(7)ME(8),and adjusts the edges by the weight ratio policy.If no k paths fulfills the requirement of at least k the shortest path,the algorithm returns the current result directly and uses the privacy level formula to calculate its privacy value.The algorithm is more flexible thanexisting algorithms,has high data availability,and privacy levels are quantifiable.In summary,the algorithm proposed by the weighted sequence attack combined with the intra-group diameter distance and the relative distance of the nodes can improve the privacy and validity of the social network release data.For privacy levels that cannot be achieved k,the proposed weighting ratio combined with the measures the privacy level formula of the algorithm,can increase the privacy of published data.