Privacy-preserving Collaborative Filtering Using Randomized Response

• Kikuchi Hiroaki

  Department of Frontier Media Science, School of Interdisciplinary Mathematical Sciences, Meiji University School of Information and Telecommunication Engineering, Tokai University

• Mochizuki Anna

  Graduate School of Science and Technology, Tokai University

抄録

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.

収録刊行物

• Journal of Information Processing

  Journal of Information Processing 21 (4), 617-623, 2013

  一般社団法人 情報処理学会

キーワード

• collaborative filtering
• privacy-preserving data mining
• cryptographic protocol

詳細情報

• CRID: 1390001205295353600
• NII論文ID: 110009605629
• NII論文ID: 130003384874
• NII書誌ID: AN00116647
• ISSN: 18827764
• ISSN: 18826652
• DOI: 10.2197/ipsjjip.21.617
• Web Site: http://id.nii.ac.jp/1001/00095202/
• Web Site: http://id.nii.ac.jp/1001/00095696/
• Web Site: https://www.jstage.jst.go.jp/article/ipsjjip/21/4/21_617/_pdf
• 本文言語コード: en
• データソース種別:
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• 抄録ライセンスフラグ: 使用不可