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In a wide spectrum of real-world applications, it is very important toanalyze and mine graph data such as social networks, communication networks,citation networks, and so on. However, the release of such graph data oftenraises privacy issue, and the graph privacy preservation has recently drawnmuch attention from the database community. While prior works on graph privacypreservation mainly focused on protecting the privacy of either the graphstructure only or vertex attributes only, in this paper, we propose a novelmechanism for graph privacy preservation by considering attacks from both graphstructures and vertex attributes, which transforms the original graph to aso-called kt-safe graph, via k-anonymity and t-closeness. We prove that thegeneration of a kt-safe graph is NP-hard, therefore, we propose a feasibleframework for effectively and efficiently anonymizing a graph with lowanonymization cost. In particular, we design a cost-model-based graphpartitioning approach to enable our proposed divide-and-conquer strategy forthe graph anonymization, and propose effective optimization techniques such aspruning method and a tree synopsis to improve the anonymization efficiency overlarge-scale graphs. Extensive experiments have been conducted to verify theefficiency and effectiveness of our proposed kt-safe graph generation approachon both real and synthetic data sets.

kt-Safety: Graph Release via k-Anonymity and t-Closeness (Technical Report)