Achieving private recommendations using randomized response techniques

Huseyin Polat, Wenliang Du

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

45 Scopus citations

Abstract

Collaborative filtering (CF) systems are receiving increasing attention. Data collected from users is needed for CF; however, many users do not feel comfortable to disclose data due to privacy risks. They sometimes refuse to provide information or might decide to give false data. By introducing privacy measures, it is more likely to increase users' confidence to contribute their data and to provide more truthful data. In this paper, we investigate achieving referrals using item-based algorithms on binary ratings without greatly exposing users' privacy. We propose to use randomized response techniques (RRT) to perturb users' data. We conduct experiments to evaluate the accuracy of our scheme and to show how different parameters affect our results using real data sets.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 10th Pacific-Asia Conference, PAKDD 2006, Proceedings
Pages637-646
Number of pages10
DOIs
StatePublished - Jul 14 2006
Event10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006 - Singapore, Singapore
Duration: Apr 9 2006Apr 12 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3918 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006
CountrySingapore
CitySingapore
Period4/9/064/12/06

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Polat, H., & Du, W. (2006). Achieving private recommendations using randomized response techniques. In Advances in Knowledge Discovery and Data Mining - 10th Pacific-Asia Conference, PAKDD 2006, Proceedings (pp. 637-646). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3918 LNAI). https://doi.org/10.1007/11731139_73