Privacy-preserving decision trees over vertically partitioned data
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 01, 2008
- Source ID
- 10.1145/1409620.1409624
Entities
People
- A. Scott Patterson
- Chris Clifton
- Jaideep Vaidya
- Murat Kantarcıoğlu
Organizations
- Air Force Office of Scientific Research
- Division of Computer and Network Systems
- Division of Information and Intelligent Systems
- Johns Hopkins University
- Purdue University
- Rutgers University
- University of Texas at Dallas