P2P collaborative filtering with privacy

Creative Commons License

KALELİ C., Polat H.

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, vol.18, no.1, pp.101-116, 2010 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 18 Issue: 1
  • Publication Date: 2010
  • Doi Number: 10.3906/elk-0808-21
  • Journal Name: TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.101-116
  • Keywords: Privacy, P2P, collaborative filtering, naive Bayesian classsifier, accuracy, RANDOMIZED-RESPONSE, SYSTEM
  • Anadolu University Affiliated: Yes

Abstract

With the evolution of the Internet and e-commerce, collaborative filtering (CF) and privacy-preserving collaborative filtering (PPCF) have become popular The goal in CF is to generate predictions with decent accuracy, efficiently. The main issue in PPCF, however, is achieving such a goal while preserving users' privacy Many implementations of CF and PPCF techniques proposed so far are centralized In centralized systems, data is collected and Stored by a central server for CF purposes Centralized storage poses several hazards to Users because the central server controls users' data