Privacy Preserving Subgraph Matching on Large Graphs in Cloud

The wide presence of large graph data and the increasing popularity of storing data in the cloud drive the needs for graph query processing on a remote cloud. But a fundamental challenge is to process user queries without compromising sensitive information. This work focuses on privacy preserving subgraph matching in a cloud server. The goal is to minimize the overhead on both cloud and client sides for subgraph matching, without compromising users' sensitive information. To that end, we transform an original graph $G$ into a privacy preserving graph G^k, which meets the requirement of an existing privacy model known as k-automorphism. By making use of the symmetry in a k-automorphic graph, a subgraph matching query can be efficiently answered using a graph G^o, a small subset of G^k. This approach saves both space and query cost in the cloud server. We also anonymize the query graphs to protect their label information using label generalization technique. To reduce the search space for a subgraph matching query, we propose a cost model to select the more effective label combinations. The effectiveness and efficiency of our method are demonstrated through extensive experimental results on real datasets.