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Cited by 262

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Privacy Preserving Naïve Bayes Classifier for Vertically Partitioned Data

Vaidya, Jaideep, Clifton, Chris·2004·Proceedings of the 2004 SIAM International Conference on Data Mining

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. Nave Bayes is often used as a baseline classifier, consistently providing reasonable classification performance. This paper brings privacy-preservation to Nave Bayes classification on vertically partitioned data.

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Metadata

Authors
Vaidya, J., & Clifton, C.
Venue
Proceedings of the 2004 SIAM International Conference on Data Mining
Published
2004-04-22
Pages
522-526
OpenAlex
W199752024
Cited by
262
References
2
FWCI
12.58
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Privacy Preserving Naïve Bayes Classifier for Vertically Partitioned Data. https://app.cochat.ai/papers/10.1137/1.9781611972740.59
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Vaidya, J., & Clifton, C. (2004). Privacy Preserving Naïve Bayes Classifier for Vertically Partitioned Data. Proceedings of the 2004 SIAM International Conference on Data Mining. https://doi.org/10.1137/1.9781611972740.59

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.