STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Multi party computations: past and present
PODC '97 Proceedings of the sixteenth annual ACM symposium on Principles of distributed computing
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Machine Learning
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Building decision tree classifier on private data
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
Simplifying Decision Trees
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Privacy Preserving ID3 Algorithm over Horizontally Partitioned Data
PDCAT '05 Proceedings of the Sixth International Conference on Parallel and Distributed Computing Applications and Technologies
Protocols for secure computations
SFCS '82 Proceedings of the 23rd Annual Symposium on Foundations of Computer Science
Privacy preserving ID3 using Gini Index over horizontally partitioned data
AICCSA '08 Proceedings of the 2008 IEEE/ACS International Conference on Computer Systems and Applications
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The information age has enabled many organizations to gather huge volumes of data. A scenario in which two parties owning confidential databases wish to run a data mining algorithm on the union of their databases, without illuminating any unnecessary information requires the protection of the privileged information. The aim of a classification problem is to classify transactions into one of a discrete set of possible categories. The secure multiparty computation problems that need to be solved at this point of time are to find the class value with the most transactions and to determine whether all the transactions have the same class attribute. In this paper we demonstrate the difference between gini index and entropy attribute measures and prove that pruning results in accuracy and privacy.