Robustness analysis of privacy-preserving model-based recommendation schemes

BİLGE A., Gunes I., Polat H.

EXPERT SYSTEMS WITH APPLICATIONS, vol.41, no.8, pp.3671-3681, 2014 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 41 Issue: 8
  • Publication Date: 2014
  • Doi Number: 10.1016/j.eswa.2013.11.039
  • Journal Name: EXPERT SYSTEMS WITH APPLICATIONS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.3671-3681
  • Keywords: Robustness, Shilling, Privacy, Recommendation, Model, Collaborative filtering, SYSTEMS, ATTACKS
  • Anadolu University Affiliated: Yes

Abstract

Privacy-preserving model-based recommendation methods are preferable over privacy-preserving memory-based schemes due to their online efficiency. Model-based prediction algorithms without privacy concerns have been investigated with respect to shilling attacks. Similarly, various privacy-preserving model-based recommendation techniques have been proposed to handle privacy issues. However, privacy-preserving model-based collaborative filtering schemes might be subjected to shilling or profile injection attacks. Therefore, their robustness against such attacks should be scrutinized.