Privacy preserving Naïve Bayes classifier for vertically partitioned data

Jaideep Vaidya; Chris Clifton

doi:10.1137/1.9781611972740.59

Privacy preserving Naïve Bayes classifier for vertically partitioned data

Jaideep Vaidya, Chris Clifton

Research output: Contribution to conference › Paper › peer-review

104 Scopus citations

Abstract

Privacy-Preserving Data Mining - developing models without seeing the data - is receiving growing attention. This paper assumes a privacy-preserving distributed data mining scenario: data sources collaborate to develop a global model, but must not disclose their data to others. Naïve Bayes is often used as a baseline classifier, consistently providing reasonable classification performance. This paper brings privacy-preservation to Naïve Bayes classification on vertically partitioned data.

Original language	English (US)
Pages	522-526
Number of pages	5
DOIs	https://doi.org/10.1137/1.9781611972740.59
State	Published - 2004
Externally published	Yes
Event	Proceedings of the Fourth SIAM International Conference on Data Mining - Lake Buena Vista, FL, United States Duration: Apr 22 2004 → Apr 24 2004

Other

Other	Proceedings of the Fourth SIAM International Conference on Data Mining
Country/Territory	United States
City	Lake Buena Vista, FL
Period	4/22/04 → 4/24/04

All Science Journal Classification (ASJC) codes

General Mathematics

Keywords

Distributed classification
Privacy
Security

Access to Document

10.1137/1.9781611972740.59

Cite this

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abstract = "Privacy-Preserving Data Mining - developing models without seeing the data - is receiving growing attention. This paper assumes a privacy-preserving distributed data mining scenario: data sources collaborate to develop a global model, but must not disclose their data to others. Na{\"i}ve Bayes is often used as a baseline classifier, consistently providing reasonable classification performance. This paper brings privacy-preservation to Na{\"i}ve Bayes classification on vertically partitioned data.",

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AU - Clifton, Chris

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AB - Privacy-Preserving Data Mining - developing models without seeing the data - is receiving growing attention. This paper assumes a privacy-preserving distributed data mining scenario: data sources collaborate to develop a global model, but must not disclose their data to others. Naïve Bayes is often used as a baseline classifier, consistently providing reasonable classification performance. This paper brings privacy-preservation to Naïve Bayes classification on vertically partitioned data.

KW - Distributed classification

KW - Privacy

KW - Security

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ER -

Privacy preserving Naïve Bayes classifier for vertically partitioned data

Abstract

Other

All Science Journal Classification (ASJC) codes

Keywords

Access to Document

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