AuthGraph: Authorized Search over Encrypted Social Graph Database with Trusted Hardware

Privacy-preserving social graph search allows the retrieval of relationships within social networks while not com promising individuals' private information. Although numerous solutions enable conjunctive queries for relationships on encrypted social networks, the multi-client model is neglected despite its crucial role in collaborative data sharing, and personalized recommendations. In this paper, we present AuthGraph, a privacy-preserving and conjunctive social graph search system with trusted hardware in the multi-client (i.e., multi-writer/multi reader) model. In AuthGraph, a data owner delegates update rights to writers for maintaining dynamic social relationships, while readers are allowed to perform edge-weighted conjunctive queries via writer-enforced access policy. Technically, AuthGraph builds a multi-writer/multi-reader model with access control delegation for writers via a set-constrained pseudo-random function, and uses attribute-based encryption to configure authorizations for readers. To give a provably secure conjunctive search system over a dynamic social graph database, AuthGraph revisits oblivious dynamic cross tag protocol via providing comprehensive forward privacy and Type-O backward privacy. Different from previous solutions, in AuthGraph, the semi-black-box deployment of trusted hardware effectively ensures system security while maintaining performance. Finally, we demonstrate the performance of AuthGraph through extensive experiments on real social network datasets on AliCloud, revealing that the writer enforced access policy has minimal impact on search time cost.