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Secure Approximate Nearest Neighbor Search with Locality-Sensitive Hashing

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Computer Security – ESORICS 2023 (ESORICS 2023)

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Abstract

Ensuring both security and efficiency in Nearest Neighbor Search (NNS) on large datasets remains a formidable challenge, as it often leads to substantial computation and communication costs due to the resource-intensive nature of ciphertext computations. To date, there have been some solutions that are capable of handling privacy-preserving NNS queries on big datasets. However, these approaches either impose significant communication and computational burdens or compromise security. In this paper, we introduce a novel framework, namely SecureANNS, for secure approximate nearest neighbor search in the semi-honest setting. Our approach begins by enhancing the building blocks of secure NNS, specifically the multiplexer and comparison operations, through oblivious transfer. We then adapt the plaintext Locality-Sensitive Hashing algorithm to select a smaller subset, reducing the need for extensive two-party computation. Finally, we introduce a new bucket retrieval algorithm for efficient subset retrieval. Experimental results on various datasets demonstrate that our SecureANNS achieves a speedup of 4\(\times \) and 14\(\times \) compared to two state-of-the-art methods respectively.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No. 62122092, No. 62032005).

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Authors and Affiliations

  1. College of Computer Science and Technology, National University of Defense Technology, Changsha, China

    Shang Song, Lin Liu, Rongmao Chen, Wei Peng & Yi Wang

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  1. Shang Song
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  2. Lin Liu
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  3. Rongmao Chen
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  4. Wei Peng
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  5. Yi Wang
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Correspondence to Lin Liu , Rongmao Chen or Wei Peng .

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Editors and Affiliations

  1. University of California, Irvine, CA, USA

    Gene Tsudik

  2. University of Padua, Padua, Italy

    Mauro Conti

  3. Delft University of Technology, Delft, The Netherlands

    Kaitai Liang

  4. Delft University of Technology, Delft, The Netherlands

    Georgios Smaragdakis

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Song, S., Liu, L., Chen, R., Peng, W., Wang, Y. (2024). Secure Approximate Nearest Neighbor Search with Locality-Sensitive Hashing. In: Tsudik, G., Conti, M., Liang, K., Smaragdakis, G. (eds) Computer Security – ESORICS 2023. ESORICS 2023. Lecture Notes in Computer Science, vol 14346. Springer, Cham. https://doi.org/10.1007/978-3-031-51479-1_21

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