Privacy-preserving Collaborative Filtering Using Randomized Response (Preprint)

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概要

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• 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.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.21(2013) No.4 (online)------------------------------

• 一般社団法人情報処理学会の論文
• 2013-09-15

著者

• Anna Mochizuki

  Graduate School of Science and Technology, Tokai University

• Hiroaki Kikuchi

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

• Hiroaki Kikuchi

  Department of Frontier Media Science, School of Interdisciplinary Mathematical Sciences, Meiji University

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