Privacy-preserving naïve Bayesian classifier-based recommendations on distributed data

KALELİ C., Polat H.

Computational Intelligence, vol.31, no.1, pp.47-68, 2015 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 31 Issue: 1
  • Publication Date: 2015
  • Doi Number: 10.1111/coin.12012
  • Journal Name: Computational Intelligence
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.47-68
  • Keywords: privacy, recommendation, distributed data, naive Bayesian classifier, electronic commerce, RANDOMIZED-RESPONSE, MULTIPARTY
  • Anadolu University Affiliated: Yes


© 2013 Wiley Periodicals, Inc.Data collected for recommendation purposes might be distributed among various e-commerce sites, which can collaboratively provide more accurate predictions. However, because of privacy concerns, they might not want to work together. If privacy measures are provided, they may decide to become involved in prediction generation processes. We propose privacy-preserving schemes eliminating e-commerce sites' privacy concerns for providing predictions on distributed data. We investigate how to achieve naïve Bayesian classifier-based recommendations when data are distributed horizontally or vertically among multiple parties, even competing ones, without greatly violating their confidentiality. We analyze our schemes in terms of privacy and additional costs and show that they do not deeply violate online vendors' secrecy and they cause insignificant overhead costs. We also perform experiments on real data, evaluate our outcomes, and provide suggestions. Our empirical results show that our schemes produce more accurate predictions.