Efficiently computing private recommendations

Z. Erkin, Zekeriya Erkin, M. Beye, T. Veugen, R.L. Lagendijk

    Research output: Contribution to conferencePaper

    25 Citations (Scopus)
    13 Downloads (Pure)

    Abstract

    Online recommender systems enable personalized service to users. The underlying collaborative filtering techniques operate on privacy sensitive user data, which could be misused by the service provider. To protect user privacy, we propose to encrypt the data and generate recommendations by processing them under encryption. Thus, the service provider observes neither user preferences nor recommendations. The proposed method uses homomorphic encryption and secure multi-party computation (MPC) techniques, which introduce a significant overhead in computational complexity. We minimize the introduced overhead by packing data and using cryptographic protocols particularly developed for this purpose. The proposed cryptographic protocol is implemented to test its correctness and performance.
    Original languageUndefined
    Pages5864-5867
    Number of pages4
    DOIs
    Publication statusPublished - 2011
    EventIEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2011 - Prague, Czech Republic
    Duration: 22 May 201127 May 2011

    Conference

    ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2011
    Abbreviated titleICASSP
    CountryCzech Republic
    CityPrague
    Period22/05/1127/05/11

    Keywords

    • EWI-23715
    • IR-87259

    Cite this

    Erkin, Z., Erkin, Z., Beye, M., Veugen, T., & Lagendijk, R. L. (2011). Efficiently computing private recommendations. 5864-5867. Paper presented at IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2011, Prague, Czech Republic. https://doi.org/10.1109/ICASSP.2011.5947695