Privacy-preserving decision trees over vertically partitioned data

Jaideep Vaidya, Chris Clifton, Murat Kantarcioglu, A. Scott Patterson

Research output: Contribution to journalArticlepeer-review

90 Scopus citations


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.

Original languageEnglish (US)
Article number14
JournalACM Transactions on Knowledge Discovery from Data
Issue number3
StatePublished - Oct 1 2008

All Science Journal Classification (ASJC) codes

  • Computer Science(all)


  • Decision tree classification
  • Privacy


Dive into the research topics of 'Privacy-preserving decision trees over vertically partitioned data'. Together they form a unique fingerprint.

Cite this