Paper 2019/359

SANNS: Scaling Up Secure Approximate k-Nearest Neighbors Search

Hao Chen, Ilaria Chillotti, Yihe Dong, Oxana Poburinnaya, Ilya Razenshteyn, and M. Sadegh Riazi


The $k$-Nearest Neighbor Search ($k$-NNS) is the backbone of several cloud-based services such as recommender systems, face recognition, and database search on text and images. In these services, the client sends the query to the cloud server and receives the response in which case the query and response are revealed to the service provider. Such data disclosures are unacceptable in several scenarios due to the sensitivity of data and/or privacy laws. In this paper, we introduce SANNS, a system for secure $k$-NNS that keeps client's query and the search result confidential. SANNS comprises two protocols: an optimized linear scan and a protocol based on a novel sublinear time clustering-based algorithm. We prove the security of both protocols in the standard semi-honest model. The protocols are built upon several state-of-the-art cryptographic primitives such as lattice-based additively homomorphic encryption, distributed oblivious RAM, and garbled circuits. We provide several contributions to each of these primitives which are applicable to other secure computation tasks. Both of our protocols rely on a new circuit for the approximate top-$k$ selection from $n$ numbers that is built from $O(n + k^2)$ comparators. We have implemented our proposed system and performed extensive experimental results on four datasets in two different computation environments, demonstrating more than $18-31\times$ faster response time compared to optimally implemented protocols from the prior work. Moreover, SANNS is the first work that scales to the database of 10 million entries, pushing the limit by more than two orders of magnitude.

Note: Further clarifications and exposition improvements

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Publication info
Published elsewhere. USENIX Security Symposium 2020
nearest neighbor searchprivacy-preserving data analysissublinear algorithms
Contact author(s)
ilya razenshteyn @ gmail com
2020-03-08: last of 4 revisions
2019-04-10: received
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      author = {Hao Chen and Ilaria Chillotti and Yihe Dong and Oxana Poburinnaya and Ilya Razenshteyn and M.  Sadegh Riazi},
      title = {SANNS: Scaling Up Secure Approximate k-Nearest Neighbors Search},
      howpublished = {Cryptology ePrint Archive, Paper 2019/359},
      year = {2019},
      note = {\url{}},
      url = {}
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