Robustness analysis of privacy-preserving model-based recommendation schemes

BİLGE A., Gunes I., Polat H.

EXPERT SYSTEMS WITH APPLICATIONS, cilt.41, sa.8, ss.3671-3681, 2014 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 41 Sayı: 8
  • Basım Tarihi: 2014
  • Doi Numarası: 10.1016/j.eswa.2013.11.039
  • Dergi Adı: EXPERT SYSTEMS WITH APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.3671-3681
  • Anahtar Kelimeler: Robustness, Shilling, Privacy, Recommendation, Model, Collaborative filtering, SYSTEMS, ATTACKS
  • Anadolu Üniversitesi Adresli: Evet

Özet

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.