A Privacy Mechanism for Access Controlled Graph Data

There has been significant interest in the development of anonymization schemes for publishing graph data. However, privacy is a major concern in dealing with graph data. In this paper, an integrated framework for ensuring privacy in the presence of an authorization mechanism is proposed. Access control mechanisms provide additional safeguard against data breaches and ensure that only authorized information is available to end-users based on their assigned roles. The integrated framework highlights a tradeoff between privacy and authorized privileges. To attain a pre-specified privacy level, access privileges might need to be relaxed. For the proposed framework, we formulate the k-anonymous Bi-objective Graph Partitioning (k-BGP) problem and provide its hardness results. Heuristics solutions are developed to solve the constraint problem. The framework provides an anonymous view based on the target class of role-based workloads for graph data. The proposed heuristics are empirically evaluated and a detailed security analysis of the framework in terms of risk associated with re-identification attack is conducted.