Effects of Inconsistently Masked Data Using RPT on CF with Privacy

Polat H., Du W.

22nd ACM Symposium on Applied Computing, Seoul, South Korea, 11 - 15 March 2007, pp.649-650 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1145/1244002.1244151
  • City: Seoul
  • Country: South Korea
  • Page Numbers: pp.649-650
  • Keywords: Inconsistently perturbed data, privacy, accuracy, CF, RPT
  • Anadolu University Affiliated: Yes


Randomized perturbation techniques (RPT) are applied to perturb the customers' private data to protect privacy while providing accurate referrals. In the RPT-based collaborative filtering (CF) with privacy schemes, proposed so far, users disguise their ratings in the same way to achieve consistently perturbed data. However, since users might have different levels of concerns about their privacy, the customers might decide to perturb their private data differently, which causes inconsistently masked data. How, then, can e-companies present referrals using such data and how can inconsistent data disguising affect accuracy and privacy?