Differentially Private Ensemble Classifiers for Data Streams

Abstract

Learning from continuous data streams via classification/regression is prevalent in many domains. Adapting to evolving data characteristics (concept drift) while protecting data owners’ private information is an open challenge. We present a differentially private ensemble solution to this problem with two distinguishing features: it allows an unbounded number of ensemble updates to deal with the potentially never-ending data streams under a fixed privacy budget, and it is extit{model agnostic} in that it treats the differentially private classification/regression model construction as a black-box. Our method outperforms competitors on real-world and simulated datasets for varying settings of privacy, concept drift, and data distribution.