Privacy-preserving decision trees over vertically partitioned data

Jaideep Vaidya, Chris Clifton

Research output: Contribution to journalConference article

39 Citations (Scopus)

Abstract

Privacy and security concerns can prevent sharing of data, derailing data mining projects. Distributed knowledge discovery, if done correctly, can alleviate this problem. In this paper, we tackle the problem of classification. We introduce a generalized privacy preserving variant of the ID3 algorithm for vertically partitioned data distributed over two or more parties. Along with the algorithm, we give a complete proof of security that gives a tight bound on the information revealed.

Original languageEnglish (US)
Pages (from-to)139-152
Number of pages14
JournalLecture Notes in Computer Science
Volume3654
StatePublished - Oct 19 2005
Event19th Annual IFIP WG 11.3 Working Conference on Data and Applications Security - Storrs, CT, United States
Duration: Aug 7 2005Aug 10 2005

Fingerprint

Privacy Preserving
Decision trees
Decision tree
Data mining
Knowledge Discovery
Privacy
Data Mining
Sharing

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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abstract = "Privacy and security concerns can prevent sharing of data, derailing data mining projects. Distributed knowledge discovery, if done correctly, can alleviate this problem. In this paper, we tackle the problem of classification. We introduce a generalized privacy preserving variant of the ID3 algorithm for vertically partitioned data distributed over two or more parties. Along with the algorithm, we give a complete proof of security that gives a tight bound on the information revealed.",
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Privacy-preserving decision trees over vertically partitioned data. / Vaidya, Jaideep; Clifton, Chris.

In: Lecture Notes in Computer Science, Vol. 3654, 19.10.2005, p. 139-152.

Research output: Contribution to journalConference article

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