Privacy preserving Naïve Bayes classifier for vertically partitioned data

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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. 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.

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APA

Vaidya, J., & Clifton, C. (2004). Privacy preserving Naïve Bayes classifier for vertically partitioned data. In SIAM Proceedings Series (pp. 522–526). Society for Industrial and Applied Mathematics Publications. https://doi.org/10.1137/1.9781611972740.59

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