62 / 2015-10-08 17:20:56

Social Network Data Anonymous Publishing Based on Evolutionary Algorithm

7952

Abstract—Based on the needs of scientific development, a growing number of social network data to be shared and released. In order to ensure that the privacy of the individual's privacy is not leaked, privacy protection should be carried on before releasing social networks data. For re-identification attack of the node degree, we proposed an improved evolutionary algorithm that to carry out the k-degree anonymity for social networks data. This paper improved fitness function and end condition of the loop of EAGA algorithm, and obtained an optimal k-degree anonymous sequence. Then we obtained the optimal anonymous social network graph of k-degree by construct anonymous graph based on k-degree anonymous sequence that previous algorithms was generated. Experimental results show that the improved evolutionary algorithm not only reduces the modification of the original social network graph, keep the property of the graph structure is better than EAGA algorithms.