Efficient Verifiable Range and Closest Point Queries in Zero-Knowledge

Esha Ghosh, Olga Ohrimenko, Roberto Tamassia
2016 Proceedings on Privacy Enhancing Technologies  
We present an efficient method for answering one-dimensional range and closest-point queries in a verifiable and privacy-preserving manner. We consider a model where a data owner outsources a dataset of key-value pairs to a server, who answers range and closest-point queries issued by a client and provides proofs of the answers. The client verifies the correctness of the answers while learning nothing about the dataset besides the answers to the current and previous queries. Our work yields for
more » ... Our work yields for the first time a zero-knowledge privacy assurance to authenticated range and closest-point queries. Previous work leaked the size of the dataset and used an inefficient proof protocol. Our construction is based on hierarchical identity-based encryption. We prove its security and analyze its efficiency both theoretically and with experiments on synthetic and real data (Enron email and Boston taxi datasets).
doi:10.1515/popets-2016-0045 dblp:journals/popets/GhoshOT16 fatcat:w2wmoaepcvg63cwxvjwdyo7lhy