Anti-Collusion Privacy-Preserving Data Mining

• Item type: National Convention(1)
• 公開日: 2010-03-08
• タイトル / 言語: en
• タイトル: Anti-Collusion Privacy-Preserving Data Mining
• 言語: eng
• 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: 東京大学情報理工学研究科
• 著者所属: 東京大学情報基盤センター
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, The University of Tokyo
• 著者所属(英): en
• 著者所属(英): Information Technology Center, The University of Tokyo
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Up to now, numerous methods of privacy-preserving data mining have been proposed. Most of them are under the assumption of semi-honest and without collusion, which means that every party follows the protocol properly with the exception that it keeps a record of all its intermediate computations but does not share the record to any others. In this study, we consider the problem of multiparty secure computation of some functions on secure summations of data spread around multiple parties. In particular, we focus our attention on the problem of collusions, in which some parties may collude to deduce the privacy of other parties. We propose a new method satisfying a high level security, full-privacy. By this method, nothing in a party is revealed even though all other parties collude. This method is also an efficient one with running time of O(m), and it can solve a large number of problems in privacy-preserving data mining.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00349328
• 書誌情報: 全国大会講演論文集; 巻 第72回, 号 セキュリティ, p. 675-676, 発行日 2010-03-08
• 出版者 / 言語: ja
• 出版者: 情報処理学会