@inproceedings{oai:ipsj.ixsq.nii.ac.jp:00201336,
 author = {Ke, Huang and Satsuya, Ohata and Kanta, Matsuura and Ke, Huang and Satsuya, Ohata and Kanta, Matsuura},
 book = {コンピュータセキュリティシンポジウム2019論文集},
 month = {Oct},
 note = {Secure multi-party computation (MPC) allows a set of parties to jointly compute a function, while keeping their inputs private. MPC has many applications, and we focus on privacy-preserving nearest neighbor search (NNS) in this paper. The purpose of the NNS is to find the closest vector to a query from a given database, and NNS arises in many fields of applications such as computer vision. Recently, some approximation methods of NNS have been proposed for speeding up the search. In this paper, we consider the combination between approximate NNS based on "short code" (searching with quantization) and MPC. We implement a short code-based privacy-preserving approximate NNS on secret sharing-based two-party computation and report some experimental results. These results help us to explore more efficient privacy-preserving approximate NNS in the future., Secure multi-party computation (MPC) allows a set of parties to jointly compute a function, while keeping their inputs private. MPC has many applications, and we focus on privacy-preserving nearest neighbor search (NNS) in this paper. The purpose of the NNS is to find the closest vector to a query from a given database, and NNS arises in many fields of applications such as computer vision. Recently, some approximation methods of NNS have been proposed for speeding up the search. In this paper, we consider the combination between approximate NNS based on "short code" (searching with quantization) and MPC. We implement a short code-based privacy-preserving approximate NNS on secret sharing-based two-party computation and report some experimental results. These results help us to explore more efficient privacy-preserving approximate NNS in the future.},
 pages = {297--304},
 publisher = {情報処理学会},
 title = {Privacy-Preserving Approximate Nearest Neighbor Search: A Construction and Experimental Results},
 volume = {2019},
 year = {2019}
}