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2011 IEEE International Workshop on Information Forensics and Security
Efficient privacy preserving K-means clustering in a three-party setting
Iguacu Falls, Brazil
November 29-December 02
ISBN: 978-1-4577-1017-9
| ASCII Text | x | ||
| Michael Beye, Zekeriya Erkin, Reginald L. Lagendijk, "Efficient privacy preserving K-means clustering in a three-party setting," Information Forensics and Security, IEEE International Workshop on, pp. 1-6, 2011 IEEE International Workshop on Information Forensics and Security, 2011. | |||
| BibTex | x | ||
| @article{ 10.1109/WIFS.2011.6123148, author = {Michael Beye and Zekeriya Erkin and Reginald L. Lagendijk}, title = {Efficient privacy preserving K-means clustering in a three-party setting}, journal ={Information Forensics and Security, IEEE International Workshop on}, volume = {0}, year = {2011}, isbn = {978-1-4577-1017-9}, pages = {1-6}, doi = {http://doi.ieeecomputersociety.org/10.1109/WIFS.2011.6123148}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Information Forensics and Security, IEEE International Workshop on TI - Efficient privacy preserving K-means clustering in a three-party setting SN - 978-1-4577-1017-9 SP1 EP6 A1 - Michael Beye, A1 - Zekeriya Erkin, A1 - Reginald L. Lagendijk, PY - 2011 VL - 0 JA - Information Forensics and Security, IEEE International Workshop on ER - | |||
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.
Citation:
Michael Beye, Zekeriya Erkin, Reginald L. Lagendijk, "Efficient privacy preserving K-means clustering in a three-party setting," wifs, pp.1-6, 2011 IEEE International Workshop on Information Forensics and Security, 2011
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