Reconstructing rated items from perturbed data

Okkalioglu B. D., KOÇ M., Polat H.

NEUROCOMPUTING, cilt.207, ss.374-386, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 207
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1016/j.neucom.2016.05.014
  • Dergi Adı: NEUROCOMPUTING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.374-386
  • Anahtar Kelimeler: Data reconstruction, Noise reduction, Auxiliary information, Privacy, Randomized perturbation, Collaborative filtering, RANDOMIZED-RESPONSE, RECOMMENDATIONS, ERROR
  • Anadolu Üniversitesi Adresli: Evet

Özet

The basic idea behind privacy-preserving collaborative filtering schemes is to prevent data collectors from deriving the actual rating values and the rated items. Different data perturbation methods have been proposed to protect individual privacy. Due to different privacy concerns, users might disguise their data variably to meet their own privacy concerns. In addition to reconstructing the true rating values, data collectors might try to reconstruct the rated items.