Sensitive and Neighborhood Privacy on Shortest Paths in the Cloud
Proceedings of International Conference on Information Integration and Web-based Applications & Services
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Preserving privacy in social networking environment has been studied extensively in recent years. Although more works have adopted un-weighted graphs to model network relationships, weighted graph modeling can provide deeper analysis of the degree of relationships. Previous works on weighted graph privacy have concentrated on preserving the shortest path characteristic between pairs of vertices. Two common types of privacy have been proposed. One type of privacy tried to add random noise edge weights to the graph but still maintain the same shortest path. The other privacy, k-shortest path privacy, minimally perturbed edge weights so that there exist k shortest paths. However, the k-shortest path privacy did not consider degree attacks on the nodes of anonymized shortest paths. For example, if the adversary possesses background knowledge of node degrees on the shortest path, the true shortest path can be identified. In this work, we present a new concept called (k1, k2)-shortest path privacy to prevent such privacy breach. A published network graph with (k1, k2)-shortest path privacy has at least k1 indistinguishable shortest paths between the source and destination vertices. In addition, for the non-overlapping vertices on the k1 shortest paths, there exist at least k2 vertices with same node degree and lie on more than one shortest path. Three heuristic algorithms are proposed and experimental results showing the feasibility and characteristics of the proposed approaches are presented.