Privacy-Preserving Approximate Nearest Neighbor Search: A Construction and Experimental Results

• Item type: Symposium(1)
• 公開日: 2019-10-14
• タイトル: Privacy-Preserving Approximate Nearest Neighbor Search: A Construction and Experimental Results
• タイトル / 言語: en
• タイトル: Privacy-Preserving Approximate Nearest Neighbor Search: A Construction and Experimental Results
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: nearest neighbor search，secure computation，short code
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_5794
• 資源タイプ: conference paper
• 著者所属: Institute of Industrial Science, The University of Tokyo
• 著者所属: National Institute of Advanced Industrial Science and Technology (AIST)
• 著者所属: Institute of Industrial Science, The University of Tokyo
• 著者所属(英): en
• 著者所属(英): Institute of Industrial Science, The University of Tokyo
• 著者所属(英): en
• 著者所属(英): National Institute of Advanced Industrial Science and Technology (AIST)
• 著者所属(英): en
• 著者所属(英): Institute of Industrial Science, The University of Tokyo
• 著者名: Ke, Huang; Satsuya, Ohata; Kanta, Matsuura
• 著者名(英): Ke, Huang; Satsuya, Ohata; Kanta, Matsuura
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: 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.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: 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.
• 書誌レコードID / 識別子タイプ: NCID
• 書誌レコードID / 関連識別子: ISSN 1882-0840
• 書誌情報: コンピュータセキュリティシンポジウム2019論文集; 巻 2019, p. 297-304, 発行日 2019-10-14
• 出版者 / 言語: ja
• 出版者: 情報処理学会