Privacy-preserving collaborative filtering on vertically partitioned data

Huseyin Polat, Wenliang Du

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

27 Scopus citations

Abstract

Collaborative filtering (CF) systems are widely used by E-commerce sites to provide predictions using existing databases comprised of ratings recorded from groups of people evaluating various items, sometimes, however, such systems' ratings are split among different parties. To provide better filtering services, such parties may wish to share their data. However, due to privacy concerns, data owners do not want to disclose data. This paper presents a privacy-preserving protocol for CF grounded on vertically partitioned data. We conducted various experiments to evaluate the overall performance of our scheme.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages651-658
Number of pages8
DOIs
StatePublished - Dec 1 2005
Event9th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2005 - Porto, Portugal
Duration: Oct 3 2005Oct 7 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3721 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2005
CountryPortugal
CityPorto
Period10/3/0510/7/05

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Polat, H., & Du, W. (2005). Privacy-preserving collaborative filtering on vertically partitioned data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 651-658). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3721 LNAI). https://doi.org/10.1007/11564126_69