Effects of inconsistently masked data using RPT on CF with privacy

Huseyin Polat, Wenliang Du

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

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?.

Original languageEnglish (US)
Title of host publicationProceedings of the 2007 ACM Symposium on Applied Computing
Pages649-653
Number of pages5
DOIs
StatePublished - Oct 18 2007
Event2007 ACM Symposium on Applied Computing - Seoul, Korea, Republic of
Duration: Mar 11 2007Mar 15 2007

Publication series

NameProceedings of the ACM Symposium on Applied Computing

Other

Other2007 ACM Symposium on Applied Computing
CountryKorea, Republic of
CitySeoul
Period3/11/073/15/07

Keywords

  • Accuracy
  • CF
  • Inconsistently perturbed data
  • Privacy
  • RPT

ASJC Scopus subject areas

  • Software

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  • Cite this

    Polat, H., & Du, W. (2007). Effects of inconsistently masked data using RPT on CF with privacy. In Proceedings of the 2007 ACM Symposium on Applied Computing (pp. 649-653). (Proceedings of the ACM Symposium on Applied Computing). https://doi.org/10.1145/1244002.1244151