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

View Sample PDF

Author(s): Can Brochmann Yildizli (Sabanci University, Turkey), Thomas Pedersen (Sabanci University, Turkey), Yucel Saygin (Sabanci University, Turkey), Erkay Savas (Sabanci University, Turkey)and Albert Levi (Sabanci University, Turkey)
Copyright: 2012
Pages: 21
Source title: Cyber Crime: Concepts, Methodologies, Tools and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-61350-323-2.ch212

Keywords: Digital Crime & Forensics / Information Science Reference / IT Security and Ethics / Security & Forensics

Purchase

View on the publisher's website for pricing and purchasing information.

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.

The IRMA Community

Research IRM

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

Purchase

Abstract

Related Content

IRMA Sponsors