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Efficient Verifiable Range and Closest Point Queries in Zero-Knowledge

Efficient Verifiable Range and Closest Point Queries in Zero-Knowledge Abstract 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 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). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Proceedings on Privacy Enhancing Technologies de Gruyter

Efficient Verifiable Range and Closest Point Queries in Zero-Knowledge

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Publisher
de Gruyter
Copyright
Copyright © 2016 by the
ISSN
2299-0984
eISSN
2299-0984
DOI
10.1515/popets-2016-0045
Publisher site
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Abstract

Abstract 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 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).

Journal

Proceedings on Privacy Enhancing Technologies – de Gruyter

Published: Oct 1, 2016

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