Privacy-Preserving Collaborative Filtering based on Horizontally Partitioned Dataset

Arjan Jeckmans, Qiang Tang, Pieter H. Hartel

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    27 Citations (Scopus)
    235 Downloads (Pure)


    Nowadays, recommender systems have been increasingly used by companies to improve their services. Such systems are employed by companies in order to satisfy their existing customers and attract new ones. However, many small or medium companies do not possess adequate customer data to generate satisfactory recommendations. To solve this problem, we propose that the companies should generate recommendations based on a joint set of customer data. For this purpose, we present a privacy-preserving collaborative filtering algorithm, which allows one company to generate recommendations based on its own customer data and the customer data from other companies. The security property is based on rigorous cryptographic techniques, and guarantees that no company will leak its customer data to others. In practice, such a guarantee not only protects companies' business incentives but also makes the operation compliant with privacy regulations. To obtain precise performance figures, we implement a prototype of the proposed solution in C++. The experimental results show that the proposed solution achieves significant accuracy difference in the generated recommendations.
    Original languageUndefined
    Title of host publicationInternational Conference on Collaboration Technologies and Systems (CTS 2012)
    Place of PublicationUSA
    PublisherIEEE Computer Society
    Number of pages8
    ISBN (Print)978-1-4673-1381-0
    Publication statusPublished - May 2012
    EventInternational Conference on Collaboration Technologies and Systems, CTS 2012 - Denver, CO, USA
    Duration: 21 May 201225 May 2012

    Publication series

    PublisherIEEE Computer Society


    ConferenceInternational Conference on Collaboration Technologies and Systems, CTS 2012
    Other21-25 May 2012


    • METIS-287966
    • Homomorphic Encryption
    • Collaborative Filtering
    • IR-81217
    • EWI-22138
    • Recommender System
    • SCS-Cybersecurity
    • DIES-Data Security
    • Privacy

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