Anonymizing Shortest Paths on Social Network Graphs

Social networking is gaining enormous popularity in the past few years. However, the popularity may also bring unexpected consequences for users regarding safety and privacy concerns. To prevent privacy being breached and modeling a social network as a weighted graph, many effective anonymization techniques have been proposed. In this work, we consider the edge weight anonymity problem. In particular, to protect the weight privacy of the shortest path between two vertices on a weighted graph, we present a new concept called k-anonymous path privacy. A published social network graph with k-anonymous path privacy has at least k indistinguishable shortest paths between the source and destination vertices. Greedy-based modification algorithms and experimental results showing the feasibility and characteristics of the proposed approach are presented.