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
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
Data reduction approach for sensitive associative classification rule hiding
ADC '08 Proceedings of the nineteenth conference on Australasian database - Volume 75
Privacy-preserving Naïve Bayes classification
The VLDB Journal — The International Journal on Very Large Data Bases
A Complete (alpha,k)-Anonymity Model for Sensitive Values Individuation Preservation
ISECS '08 Proceedings of the 2008 International Symposium on Electronic Commerce and Security
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Classification is one of the most ubiquitous data mining problems found in real life. Decision tree classification is one of the best-known solution approaches. This paper describes the construction of a decision tree classifier on vertically partitioned data owned by different owners, by concealing the data held by the parties. Our protocol uses an efficient splitting strategy as well as a semi-trusted third party to efficiently build a binary decision tree model. The third party uses a commodity server where the different owners send request and receive commodities (data) from the server, where the commodities are independent of the parties involved in classification. Commodity server assists the parties to conduct the computation for decision tree construction. The security of our classification method is based on scalar product protocol. The goal of secure protocols is to provide privacy preservation, without finding a third party that everyone trusts.