Privacy-Preserving Naïve Bayesian Classifier–Based Recommendations on Distributed Data
Article first published online: 9 JUL 2013
© 2013 Wiley Periodicals, Inc.
Volume 31, Issue 1, pages 47–68, February 2015
How to Cite
2015), Privacy-Preserving Naïve Bayesian Classifier–Based Recommendations on Distributed Data, Computational Intelligence, 31, 47–68, doi: 10.1111/coin.12012, and (
- Issue published online: 10 FEB 2015
- Article first published online: 9 JUL 2013
- Manuscript Accepted: 15 MAR 2013
- Manuscript Revised: 4 DEC 2012
- Manuscript Received: 17 JAN 2012
- distributed data;
- naïve Bayesian classifier;
- electronic commerce
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.