Secure Approximate Nearest Neighbor Search with Locality-Sensitive Hashing

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 × and 14 × compared to two state-of-the-art methods respectively.