Privacy Preserving Naïve Bayes Classifier for Vertically Partitioned Data

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.