Paper 2024/1774

PANTHER: Private Approximate Nearest Neighbor Search in the Single Server Setting

Jingyu Li, Ant Group
Zhicong Huang, Ant Group
Min Zhang, Institute of Software, Chinese Academy of Sciences
Jian Liu, Zhejiang University
Cheng Hong, Ant Group
Tao Wei, Ant Group
Wenguang Chen, Ant Group
Abstract

Approximate nearest neighbor search (ANNS), also known as vector search, is an important building block for varies applications, such as databases, biometrics, and machine learning. In this work, we are interested in the private ANNS problem, where the client wants to learn (and can only learn) the ANNS results without revealing the query to the server. Previous private ANNS works either suffers from high communication cost (Chen et al., USENIX Security 2020) or works under a weaker security assumption of two non-colluding servers (Servan-Schreiber et al., SP 2022). We present Panther, an efficient private ANNS framework under the single server setting. Panther achieves its high performance via several novel co-designs of private information retrieval (PIR), secretsharing, garbled circuits, and homomorphic encryption. We made extensive experiments using Panther on four public datasets, results show that Panther could answer an ANNS query on 10 million points in 23 seconds with 318 MB of communication. This is more than 6× faster and 18× more compact than Chen et al..

Metadata
Available format(s)
PDF
Category
Applications
Publication info
Preprint.
Keywords
Private Information RetrievalHomomorphic EncryptionMultiparty ComputationNearest Neighbor Search
Contact author(s)
ljy404490 @ antgroup com
zhicong hzc @ antgroup com
zhangmin @ iscas ac cn
liujian2411 @ zju edu cn
vince hc @ antgroup com
lenx wei @ antgroup com
yuanben cwg @ antgroup com
History
2024-11-01: approved
2024-10-31: received
See all versions
Short URL
https://ia.cr/2024/1774
License
Creative Commons Attribution-NonCommercial
CC BY-NC

BibTeX

@misc{cryptoeprint:2024/1774,
      author = {Jingyu Li and Zhicong Huang and Min Zhang and Jian Liu and Cheng Hong and Tao Wei and Wenguang Chen},
      title = {{PANTHER}: Private Approximate Nearest Neighbor Search in the Single Server Setting},
      howpublished = {Cryptology {ePrint} Archive, Paper 2024/1774},
      year = {2024},
      url = {https://eprint.iacr.org/2024/1774}
}
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