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Distributed Privacy Preserving Clustering via Homomorphic Secret Sharing and Its Application to (Vertically) Partitioned Spatio-Temporal Data

Distributed Privacy Preserving Clustering via Homomorphic Secret Sharing and Its Application to (Vertically) Partitioned Spatio-Temporal Data

Can Brochmann Yildizli, Thomas Pedersen, Yucel Saygin, Erkay Savas, Albert Levi
Copyright: © 2011 |Volume: 7 |Issue: 1 |Pages: 21
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781613506332|DOI: 10.4018/jdwm.2011010103
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MLA

Yildizli, Can Brochmann, et al. "Distributed Privacy Preserving Clustering via Homomorphic Secret Sharing and Its Application to (Vertically) Partitioned Spatio-Temporal Data." IJDWM vol.7, no.1 2011: pp.46-66. http://doi.org/10.4018/jdwm.2011010103

APA

Yildizli, C. B., Pedersen, T., Saygin, Y., Savas, E., & Levi, A. (2011). Distributed Privacy Preserving Clustering via Homomorphic Secret Sharing and Its Application to (Vertically) Partitioned Spatio-Temporal Data. International Journal of Data Warehousing and Mining (IJDWM), 7(1), 46-66. http://doi.org/10.4018/jdwm.2011010103

Chicago

Yildizli, Can Brochmann, et al. "Distributed Privacy Preserving Clustering via Homomorphic Secret Sharing and Its Application to (Vertically) Partitioned Spatio-Temporal Data," International Journal of Data Warehousing and Mining (IJDWM) 7, no.1: 46-66. http://doi.org/10.4018/jdwm.2011010103

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Abstract

Recent concerns about privacy issues have motivated data mining researchers to develop methods for performing data mining while preserving the privacy of individuals. One approach to develop privacy preserving data mining algorithms is secure multiparty computation, which allows for privacy preserving data mining algorithms that do not trade accuracy for privacy. However, earlier methods suffer from very high communication and computational costs, making them infeasible to use in any real world scenario. Moreover, these algorithms have strict assumptions on the involved parties, assuming involved parties will not collude with each other. In this paper, the authors propose a new secure multiparty computation based k-means clustering algorithm that is both secure and efficient enough to be used in a real world scenario. Experiments based on realistic scenarios reveal that this protocol has lower communication costs and significantly lower computational costs.

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