Reconstructing rated items from perturbed data

Okkalioglu B. D., KOÇ M., Polat H.

NEUROCOMPUTING, vol.207, pp.374-386, 2016 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 207
  • Publication Date: 2016
  • Doi Number: 10.1016/j.neucom.2016.05.014
  • Journal Name: NEUROCOMPUTING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.374-386
  • Keywords: Data reconstruction, Noise reduction, Auxiliary information, Privacy, Randomized perturbation, Collaborative filtering, RANDOMIZED-RESPONSE, RECOMMENDATIONS, ERROR
  • Anadolu University Affiliated: Yes

Abstract

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.