On the Comparison of Classifiers' Construction over Private Inputs

Abstract

Classifiers are often trained over data collected from different sources. Sharing their data with other entities, however, can raise privacy concerns for data owners. To protect data confidentiality while being able to train a classifier, effective solutions have been proposed in the literature to construct various types of classifiers over private data. However, to date an analysis and comparison of the computation and communication costs for the construction of classifiers over private data is missing, making it difficult to determine which classifier can be used in a given application domain. In this work, we show how two well-known classifiers (Naive Bayes and SVM classifiers) can be securely build over private inputs, and evaluate their construction costs. We assess the computation and communication costs for training the classifiers both theoretically and empirically for different benchmark datasets.