Cryptology ePrint Archive: Report 2021/816

Privacy-Preserving Approximate k-Nearest-Neighbors Search that Hides Access, Query and Volume Patterns

Alexandra Boldyreva and Tianxin Tang

Abstract: We study the problem of privacy-preserving approximate kNN search in an outsourced environment the client sends the encrypted data to an untrusted server and later can perform secure approximate kNN search and updates. We design a security model and propose a generic construction based on locality-sensitive hashing, symmetric encryption, and an oblivious map. The construction provides very strong security guarantees, not only hiding the information about the data, but also the access, query, and volume patterns. We implement, evaluate efficiency, and compare the performance of two concrete schemes based on an oblivious AVL tree and an oblivious BSkiplist.

Category / Keywords: cryptographic protocols / oblivious data structures, locality-sensitive hashing, approximate kNN, search on encrypted data

Original Publication (with minor differences): PETS 2021

Date: received 15 Jun 2021

Contact author: sasha at gatech edu, ttang@gatech edu

Available format(s): PDF | BibTeX Citation

Version: 20210616:133623 (All versions of this report)

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