Shilling Attacks Against Memory-Based Privacy-Preserving Recommendation Algorithms

Gunes I., BİLGE A., Polat H.

KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, cilt.7, sa.5, ss.1272-1290, 2013 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 7 Sayı: 5
  • Basım Tarihi: 2013
  • Doi Numarası: 10.3837/tiis.2013.05.019
  • Dergi Adı: KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1272-1290
  • Anahtar Kelimeler: Shilling, privacy, robustness, recommendation, profile injection, collaborative filtering, SYSTEMS
  • Anadolu Üniversitesi Adresli: Evet

Özet

Privacy-preserving collaborative filtering schemes are becoming increasingly popular because they handle the information overload problem without jeopardizing privacy. However, they may be susceptible to shilling or profile injection attacks, similar to traditional recommender systems without privacy measures. Although researchers have proposed various privacy-preserving recommendation frameworks, it has not been shown that such schemes are resistant to profile injection attacks. In this study, we investigate two memory-based privacy-preserving collaborative filtering algorithms and analyze their robustness against several shilling attack strategies. We first design and apply formerly proposed shilling attack techniques to privately collected databases. We analyze their effectiveness in manipulating predicted recommendations by experimenting on real data-based benchmark data sets. We show that it is still possible to manipulate the predictions significantly on databases consisting of masked preferences even though a few of the attack strategies are not effective in a privacy-preserving environment.