Privacy-preserving Collaborative Filtering Using Randomized Response

• Item type: JInfP(1)
• 公開日: 2013-10-15
• タイトル: Privacy-preserving Collaborative Filtering Using Randomized Response
• タイトル / 言語: en
• タイトル: Privacy-preserving Collaborative Filtering Using Randomized Response
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [Special Issue on Computer Security Technology for Enriching the Future] collaborative filtering, privacy-preserving data mining, cryptographic protocol
• 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: Department of Frontier Media Science, School of Interdisciplinary Mathematical Sciences, Meiji University／School of Information and Telecommunication Engineering, Tokai University
• 著者所属: Graduate School of Science and Technology, Tokai University
• 著者所属(英): en
• 著者所属(英): Department of Frontier Media Science, School of Interdisciplinary Mathematical Sciences, Meiji University / School of Information and Telecommunication Engineering, Tokai University
• 著者所属(英): en
• 著者所属(英): Graduate School of Science and Technology, Tokai University
• 著者名: Hiroaki, Kikuchi; Anna, Mochizuki
• 著者名(英): Hiroaki, Kikuchi; Anna, Mochizuki
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: This paper proposes a new privacy-preserving recommendation method classified into a randomized perturbation scheme in which a user adds a random noise to the original rating value and a server provides a disguised data to allow users to predict the rating value for unseen items. The proposed scheme performs a perturbation in a randomized response scheme, which preserves a higher degree of privacy than that of an additive perturbation. To address the accuracy reduction of the randomized response, the proposed scheme uses a posterior probability distribution function, derived from Bayes' estimation for the reconstruction of the original distribution, to revise the similarity between items computed from the disguised matrix. A simple experiment shows the accuracy improvement of the proposed scheme.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: This paper proposes a new privacy-preserving recommendation method classified into a randomized perturbation scheme in which a user adds a random noise to the original rating value and a server provides a disguised data to allow users to predict the rating value for unseen items. The proposed scheme performs a perturbation in a randomized response scheme, which preserves a higher degree of privacy than that of an additive perturbation. To address the accuracy reduction of the randomized response, the proposed scheme uses a posterior probability distribution function, derived from Bayes' estimation for the reconstruction of the original distribution, to revise the similarity between items computed from the disguised matrix. A simple experiment shows the accuracy improvement of the proposed scheme.
• 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: AA00700121
• 書誌情報: Journal of information processing; 巻 21, 号 4, p. 617-623, 発行日 2013-10-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-6652
• 出版者 / 言語: ja
• 出版者: 情報処理学会