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
Protocols for secure computations
SFCS '82 Proceedings of the 23rd Annual Symposium on Foundations of Computer Science
Public-key cryptosystems based on composite degree residuosity classes
EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
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
BronzeGate: real-time transactional data obfuscation for GoldenGate
Proceedings of the 13th International Conference on Extending Database Technology
Anonymous biometric access control
EURASIP Journal on Information Security - Special issue on enhancing privacy protection in multimedia systems
Privacy-preserving back-propagation and extreme learning machine algorithms
Data & Knowledge Engineering
International Journal of Data Warehousing and Mining
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Recent concerns about privacy issues motivated data mining researchers to develop methods for performing data mining while preserving the privacy of individuals. However, the current techniques for privacy preserving data mining suffer from high communication and computation overheads which are prohibitive considering even a modest database size. Furthermore, the proposed techniques have strict assumptions on the involved parties which need to be relaxed in order to reflect the real-world requirements. In this paper we concentrate on a distributed scenario where the data is partitioned vertically over multiple sites and the involved sites would like to perform clustering without revealing their local databases. For this setting, we propose a new protocol for privacy preserving k-means clustering based on additive secret sharing. We show that the new protocol is more secure than the state of the art. Experiments conducted on real and synthetic data sets show that, in realistic scenarios, the communication and computation cost of our protocol is considerably less than the state of the art which is crucial for data mining applications.