Privacy-preserving decision trees over vertically partitioned data

Jaideep Vaidya; Chris Clifton

doi:10.1007/11535706_11

Privacy-preserving decision trees over vertically partitioned data

Jaideep Vaidya, Chris Clifton

Rutgers Business School, Management & Information Systems

Research output: Contribution to journal › Conference article › peer-review

71 Scopus citations

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 language	English (US)
Pages (from-to)	139-152
Number of pages	14
Journal	Lecture Notes in Computer Science
Volume	3654
DOIs	https://doi.org/10.1007/11535706_11
State	Published - 2005
Event	19th Annual IFIP WG 11.3 Working Conference on Data and Applications Security - Storrs, CT, United States Duration: Aug 7 2005 → Aug 10 2005

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

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10.1007/11535706_11

Cite this

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title = "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. 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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AB - 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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JF - Lecture Notes in Computer Science

T2 - 19th Annual IFIP WG 11.3 Working Conference on Data and Applications Security

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ER -

Privacy-preserving decision trees over vertically partitioned data

Abstract

All Science Journal Classification (ASJC) codes

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