Accuracy of privacy-preserving collaborative filtering based on quasi-homomorphic similarity

Hiroaki Kikuchi, Yoshiki Aoki, Masayuki Terada, Kazuhiko Ishii, Kimihiko Sekino

Research output: Contribution to conferencePaper

2 Citations (Scopus)

Abstract

We study the problem of predicting a rating for an unseen item based on a distributed dataset owned by two honestbut- curious parties without revealing their private datasets to each other. Our proposed idea uses a new similarity measure such that the similarity aggregated from two local similarities is approximately equal to the global similarity. We evaluate the accuracy of prediction of rating and clarify the lower bound of estimation error and the expected value of error to be small enough to approximate the global prediction. We also show a new privacy preserving collaborative protocol with light weight overhead.

Original languageEnglish
Pages555-562
Number of pages8
DOIs
Publication statusPublished - 28 Nov 2012
Event9th IEEE International Conference on Ubiquitous Intelligence and Computing, UIC 2012 and 9th IEEE International Conference on Autonomic and Trusted Computing, ATC 2012 - Fukuoka, Japan
Duration: 4 Sep 20127 Sep 2012

Conference

Conference9th IEEE International Conference on Ubiquitous Intelligence and Computing, UIC 2012 and 9th IEEE International Conference on Autonomic and Trusted Computing, ATC 2012
CountryJapan
CityFukuoka
Period4/09/127/09/12

Keywords

  • Collaborative Filtering
  • Cryptographical Protocol
  • Privacy-Preserving Data Mining

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    Kikuchi, H., Aoki, Y., Terada, M., Ishii, K., & Sekino, K. (2012). Accuracy of privacy-preserving collaborative filtering based on quasi-homomorphic similarity. 555-562. Paper presented at 9th IEEE International Conference on Ubiquitous Intelligence and Computing, UIC 2012 and 9th IEEE International Conference on Autonomic and Trusted Computing, ATC 2012, Fukuoka, Japan. https://doi.org/10.1109/UIC-ATC.2012.131