Classification Learning from Private Data in Heterogeneous Settings

Abstract

Classification is useful for mining labels of data. Though well-trained classifiers benefit many applications, their training procedures on user-contributed data may leak users’ privacy.

This work studies methods for private model training in heterogeneous settings, specially for the Naïve Bayes Classifier (NBC). Unlike previous works focusing on centralized and consistent datasets, we consider the private training in two more practical settings, namely the local setting and the mixture setting. In the local setting, individuals directly contribute training tuples to the untrusted trainer. In the mixture setting, the training dataset is composed of individual tuples and statistics of datasets from institutes. We propose a randomized response based NBC strategy for the local setting. To cope with the privacy of heterogeneous data (single tuples and the statistics) in the mixture setting, we design a unified privatized scheme. It integrates respective sanitization strategies on the two data types while preserving privacy. Besides contributing error bounds of estimated probabilities constituting NBC, we prove their optimality in the minimax framework and quantify the classification error of the privately learned NBC. Our analyses are validated with extensive experiments on real-world datasets.