General criteria on building decision trees for data classification
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Secure two and multi-party association rule mining
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
Anonymous biometric access control
EURASIP Journal on Information Security - Special issue on enhancing privacy protection in multimedia systems
A classification based framework for privacy preserving data mining
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Privacy-preserving back-propagation and extreme learning machine algorithms
Data & Knowledge Engineering
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The ID3 algorithm is a standard, popular, and simple method for data classification and decision tree creation. Since privacy-preserving data mining should be taken into consideration, several secure multi-party computation protocols have been presented based on this technique. Entropy and Gini Index are two protocols which compute Information-Gain at each step when producing a decision tree. The Gini Index, however, has been less studied in privacy-preserving data mining protocols. In this paper, we show how Gini can be used in privacy-preserving ID3 algorithms to create decision tree classifications in such a way that involved parties can jointly compute the gain value of each normal attribute without revealing their own private information to each other, while the database is horizontally partitioned over two or more parties. Three secure multiparty sub-protocols are presented to evaluate the intermediate computations. The communication overhead has been kept reasonably low to make the whole protocol efficient and practical.