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Privacy‐Preserving Naïve Bayesian Classifier–Based Recommendations on Distributed Data
Author(s) -
Kaleli Cihan,
Polat Huseyin
Publication year - 2015
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/coin.12012
Subject(s) - computer science , confidentiality , classifier (uml) , secrecy , information privacy , data mining , bayesian probability , naive bayes classifier , machine learning , artificial intelligence , internet privacy , computer security , support vector machine
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.