STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Multiparty unconditionally secure protocols
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
Designs, Codes and Cryptography - Special issue on towards a quarter-century of public key cryptography
Data mining: concepts and techniques
Data mining: concepts and techniques
Advances in Distributed and Parallel Knowledge Discovery
Advances in Distributed and Parallel Knowledge Discovery
Tools for privacy preserving distributed data mining
ACM SIGKDD Explorations Newsletter
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
On the Privacy Preserving Properties of Random Data Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
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 Data Mining: Why, How, and When
IEEE Security and Privacy
A new scheme on privacy-preserving data classification
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Privacy-preserving distributed association rule mining via semi-trusted mixer
Data & Knowledge Engineering
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Protocols for secure computations
SFCS '82 Proceedings of the 23rd Annual Symposium on Foundations of Computer Science
How to generate and exchange secrets
SFCS '86 Proceedings of the 27th 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
A public key cryptosystem and a signature scheme based on discrete logarithms
IEEE Transactions on Information Theory
TrustBus'11 Proceedings of the 8th international conference on Trust, privacy and security in digital business
Arbitrarily distributed data-based recommendations with privacy
Data & Knowledge Engineering
Data cloud for distributed data mining via pipelined mapreduce
ADMI'11 Proceedings of the 7th international conference on Agents and Data Mining Interaction
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Distributed data mining applications, such as those dealing with health care, finance, counter-terrorism and homeland defense, use sensitive data from distributed databases held by different parties. This comes into direct conflict with an individual's need and right to privacy. It is thus of great importance to develop adequate security techniques for protecting privacy of individual values used for data mining. In this paper, we consider privacy-preserving naive Bayes classifier for horizontally partitioned distributed data and propose a two-party protocol and a multi-party protocol to achieve it. Our multi-party protocol is built on the semi-trusted mixer model, in which each data site sends messages to two semi-trusted mixers, respectively, which run our two-party protocol and then broadcast the classification result. This model facilitates both trust management and implementation. Security analysis has showed that our two-party protocol is a private protocol and our multi-party protocol is a private protocol as long as the two mixers do not conclude.