Privacy-preserving decision trees over vertically partitioned data
- 27 October 2008
- journal article
- research article
- Published by Association for Computing Machinery (ACM) in ACM Transactions on Knowledge Discovery From Data
- Vol. 2 (3), 1-27
- https://doi.org/10.1145/1409620.1409624
Abstract
Privacy and security concerns can prevent sharing of data, derailing data-mining projects. Distributed knowledge discovery, if done correctly, can alleviate this problem. We introduce a generalized privacy-preserving variant of the ID3 algorithm for vertically partitioned data distributed over two or more parties. Along with a proof of security, we discuss what would be necessary to make the protocols completely secure. We also provide experimental results, giving a first demonstration of the practical complexity of secure multiparty computation-based data mining.Keywords
Funding Information
- Division of Information and Intelligent Systems (CNS-0746943IIS-0428168)
- Air Force Office of Scientific Research (FA9550-07-1-0041, FA9550-08-1-0265)
- Division of Computer and Network Systems (CNS-0746943IIS-0428168)
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