Privacy-preserving collaborative filtering using randomized perturbation techniques

Huseyin Polat, Wenliang Du

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

192 Scopus citations

Abstract

Collaborative Filtering (CF) techniques are becoming increasingly popular with the evolution of the Internet. To conduct collaborative filtering, data from customers are needed. However, collecting high quality data from customers is not an easy task because many customers are so concerned about their privacy that they might decide to give false information. We propose a randomized perturbation (RP) technique to protect users' privacy while still producing accurate recommendations.

Original languageEnglish (US)
Title of host publicationProceedings - 3rd IEEE International Conference on Data Mining, ICDM 2003
Pages625-628
Number of pages4
StatePublished - Dec 1 2003
Event3rd IEEE International Conference on Data Mining, ICDM '03 - Melbourne, FL, United States
Duration: Nov 19 2003Nov 22 2003

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other3rd IEEE International Conference on Data Mining, ICDM '03
CountryUnited States
CityMelbourne, FL
Period11/19/0311/22/03

ASJC Scopus subject areas

  • Engineering(all)

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