Protecting Privacy Technology Of Subgraph About Social Network In Cloud Environment

With the rapid development of information technology,the amount of data in social networks has become larger.Because social networks contain a lot of valuable information,more and more attention is being paid to data mining in data of social networks.The social network contains a large number of users' personal information.If the social network data is used incorrectly,it will lead to the leakage of the user's private data and endanger the personal information security.In order to achieve the purpose of protecting personal privacy data in social networks,many mature technologies and research results about social network privacy protection have been proposed at this stage.As a tool for storing and processing large-scale social network data,the cloud platform has gradually become mainstream.In order to solve the problem of large-scale social network subgraph matching privacy protection,a distributed K-automorphic social network privacy protection algorithm is proposed.The noise edges are added by passing the marked information between the vertices,and the original picture is anonymized as a K-automorphic social network map with K symmetric subgraphs.A distributed subgraph matching method is proposed to perform subgraph matching on the uploaded graph.The query graph is decomposed according to the selectivity of the node in the query graph to obtain a query decomposition subgraph.The subgraph matching in run in each computing node with the distributed parallel way.Searching for the decomposition subgraph matching result,and connecting the results to obtain the matching result of the query graph;recovering obtained subgraph matching results according to the symmetry of the K-automorphism social network graph and the K automorphism functions in the client and filtering to get the correct matching result lastly.Because the data in the cloud environment is constantly updated,the subgraph matching results will change at any time.If the graph data is not updated once and the subgraph matching operation is performed again,a lot of time cost is wasted.In response to this situation,a subgraph matching privacy protection technique based onthe incremental method is proposed.The method adds two auxiliary data structure:matching sets and candidate sets and utilizes auxiliary structures to simplify the matching operation for the graph data in which the node has changed,thereby achieving the purpose of saving the matching time.We built the distributed graph processing platform GraphEngine.Two methods are implemented on the distributed graph processing platform,and the real data sets roadNet-CA and roadNet-PA are used to test the execution efficiency and space cost of the above two methods.The experimental results show that the distributed Kautomorphic social network privacy protection algorithm improves the efficiency of processing large-scale graph data while the distributed subgraph matching method improves the matching efficiency and ensures the correct rate of matching results.Based on the incremental method,the subgraph matching privacy protection technology improves the efficiency of subgraph matching in the case of dynamic social networks.