Completeness theorems for non-cryptographic fault-tolerant distributed computation
STOC '88 Proceedings of the twentieth annual ACM symposium on Theory of computing
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A Cost-Effective Pay-Per-Multiplication Comparison Method for Millionaires
CT-RSA 2001 Proceedings of the 2001 Conference on Topics in Cryptology: The Cryptographer's Track at RSA
Tools for privacy preserving distributed data mining
ACM SIGKDD Explorations Newsletter
Non-Interactive CryptoComputing For NC1
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-preserving Bayesian network structure computation on distributed heterogeneous data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Cryptographically private support vector machines
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Distributed privacy preserving k-means clustering with additive secret sharing
PAIS '08 Proceedings of the 2008 international workshop on Privacy and anonymity in information society
Privacy-Preserving Data Mining: Models and Algorithms
Privacy-Preserving Data Mining: Models and Algorithms
Efficient privacy preserving distributed clustering based on secret sharing
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Journal of Computer and System Sciences
Preserving Privacy in Time Series Data Mining
International Journal of Data Warehousing and Mining
Spatial Clustering in SOLAP Systems to Enhance Map Visualization
International Journal of Data Warehousing and Mining
Weighted Fuzzy-Possibilistic C-Means Over Large Data Sets
International Journal of Data Warehousing and Mining
Mobile Peer-to-Peer data dissemination in wireless ad-hoc networks
Information Sciences: an International Journal
Indexing moving objects for directions and velocities queries
Information Systems Frontiers
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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.