A High Collusion-Resistant Approach to Distributed Privacy-preserving Data Mining

• Item type: Trans(1)
• 公開日: 2007-06-15
• タイトル: A High Collusion-Resistant Approach to Distributed Privacy-preserving Data Mining
• タイトル / 言語: en
• タイトル: A High Collusion-Resistant Approach to Distributed Privacy-preserving Data Mining
• 言語: jpn
• キーワード / 主題Scheme: Other
• キーワード / 主題: 研究論文
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Faculty of Software and Information Science Iwate Prefectural University Presently with Hitachi East Japan Solutions
• 著者所属: Faculty of Software and Information Science Iwate Prefectural University
• 著者所属: Faculty of Software and Information Science Iwate Prefectural University
• 著者所属: Faculty of Software and Information Science Iwate Prefectural University
• 著者所属(英): en
• 著者所属(英): Faculty of Software and Information Science, Iwate Prefectural University , Presently with Hitachi East Japan Solutions
• 著者所属(英): en
• 著者所属(英): Faculty of Software and Information Science, Iwate Prefectural University
• 著者所属(英): en
• 著者所属(英): Faculty of Software and Information Science, Iwate Prefectural University
• 著者所属(英): en
• 著者所属(英): Faculty of Software and Information Science, Iwate Prefectural University
• 著者名: Shintaro, Urabe; Jiahong, Wang; Eiichiro, Kodama; Toyoo, Takata
• 著者名(英): Shintaro, Urabe; Jiahong, Wang; Eiichiro, Kodama; Toyoo, Takata
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Data mining across different companies organizations online shops or the likes called sites is necessary so as to discover valuable shared patterns associations trends or dependencies in their shared data. Privacy however is a concern. In many situations it is required that data mining should be conducted without any privacy being violated. In response to this requirement this paper proposes an effective distributed privacy-preserving data mining approach called CRDM (Collusion-Resistant Data Mining). CRDM is characterized by its ability to resist the collusion. Unless all sites but the victim collude privacy of a site cannot be violated. Considering that for such applications that need not so high a level of security excess security assurance would incur extra costs in communication an extension scheme is also presented so that communication cost can be restrained while still maintaining a user-specified level of security. Results of both analytical and experimental performance study demonstrate the effectiveness of CRDM.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Data mining across different companies, organizations, online shops, or the likes, called sites, is necessary so as to discover valuable shared patterns, associations, trends, or dependencies in their shared data. Privacy, however, is a concern. In many situations it is required that data mining should be conducted without any privacy being violated. In response to this requirement, this paper proposes an effective distributed privacy-preserving data mining approach called CRDM (Collusion-Resistant Data Mining). CRDM is characterized by its ability to resist the collusion. Unless all sites but the victim collude, privacy of a site cannot be violated. Considering that for such applications that need not so high a level of security, excess security assurance would incur extra costs in communication, an extension scheme is also presented so that communication cost can be restrained while still maintaining a user-specified level of security. Results of both analytical and experimental performance study demonstrate the effectiveness of CRDM.
• 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: AA11464847
• 書誌情報: 情報処理学会論文誌データベース（TOD）; 巻 48, 号 SIG11(TOD34), p. 104-117, 発行日 2007-06-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7799
• 出版者 / 言語: ja
• 出版者: 情報処理学会