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

Jingyu Li, Zhicong Huang, Min Zhang, Jian Liu, Cheng Hong, Tao Wei, Wenguang Chen

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..