@article{oai:ipsj.ixsq.nii.ac.jp:00095718, author = {Hiroaki, Kikuchi and Anna, Mochizuki and Hiroaki, Kikuchi and Anna, Mochizuki}, issue = {4}, journal = {Journal of information processing}, month = {Oct}, note = {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 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.}, pages = {617--623}, title = {Privacy-preserving Collaborative Filtering Using Randomized Response}, volume = {21}, year = {2013} }