Robustness analysis of privacy-preserving model-based recommendation schemes
EXPERT SYSTEMS WITH APPLICATIONS, vol.41, no.8, pp.3671-3681, 2014 (SCI-Expanded, Scopus)
- 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.