Efficient privacy preserving K-means clustering in a three-party setting

Michael Beye, Zekeriya Erkin, Reginald L. Lagendijk

    Research output: Contribution to conferencePaperAcademic

    22 Citations (Scopus)
    55 Downloads (Pure)

    Abstract

    User clustering is a common operation in online social networks, for example to recommend new friends. In previous work [5], Erkin et al. proposed a privacy-preserving K-means clustering algorithm for the semi-honest model, using homomorphic encryption and multi-party computation. This paper makes three contributions: 1) it addresses remaining privacy weaknesses in Erkin’s protocol, 2) it minimizes user interaction and allows clustering of offline users (through a central party acting on users’ behalf), and 3) it enables highly efficient non-linear operations, improving overall efficiency (by its three-party structure). Our complexity and security analyses underscore the advantages of the solution.
    Original languageEnglish
    Pages1-6
    Number of pages6
    DOIs
    Publication statusPublished - 2011
    EventIEEE International Workshop on Information Forensics and Security, WIFS 2011 - Iguacu Falls, Brazil
    Duration: 29 Nov 20112 Dec 2011

    Workshop

    WorkshopIEEE International Workshop on Information Forensics and Security, WIFS 2011
    Period29/11/112/12/11
    Other29 November - 02 December 2011

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