Privacy Preserving Approach of Published Social Networks Data with Vertex and Edge Modification Algorithm

Abstract

Background/Objectives: The main objectives are to preserve the privacy of the published data by modifying the graph by adding the smallest number of edges. Methods/Statistical analysis: In this study, a quantitative criterion for anonymity cost is defined. If the anonymity cost is numerically measurable, comparison and then selection of the best state can be possible. To calculate the cost of anonymity, due to generalization of the information on the labels of vertices, we use the NCP .This method provides a quantitative value of the lost information due to the generalization of the labels. Findings: In this research, social networks with a variety of connections and critical edges among individuals have been taken into account, and the purpose is to preserve the privacy of the published data by modifying the graph by adding the smallest number of edges. After applying anonymity for each k anonymity vertex, in addition to the same degree of vertices, connections of the vertices become identical as well. After anonymity, social network becomes resistant to neighborhood attacks. Greedy algorithm was developed for this purpose, which had an optimized performance using the modified graph and was efficient in terms of time complexity and memory consumption. The main purpose of this research is to establish the optimal balance between the privacy of entities, Usefulness of the data and the cost of anonymity. Applications/ Improvements: In order to reduce the cost of anonymityand alsoto have the lowest rate of lost information: generalization of the information on the labels of edgeshave been removed, As well as, adding and removing vertex in this methodnot permitted.