User clustering is a common operation in online social networks, for example to recommend new friends. In previous work , 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.
|Number of pages||6|
|Publication status||Published - 2011|
|Event||IEEE International Workshop on Information Forensics and Security, WIFS 2011 - Iguacu Falls, Brazil|
Duration: 29 Nov 2011 → 2 Dec 2011
|Workshop||IEEE International Workshop on Information Forensics and Security, WIFS 2011|
|Period||29/11/11 → 2/12/11|
|Other||29 November - 02 December 2011|