Differentially Private Ensemble Classifiers for Data Streams

Lovedeep Gondara, Ke Wang, Ricardo Silva Carvalho

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 anunbounded number of ensemble updates to deal with the potentially never-ending data streams under a fixed privacy budget, and it ismodel agnostic, in that it treats any pre-trained differentially private classification/regression model as a black-box. Our method outperforms competitors on real-world and simulated datasets for varying settings of privacy, concept drift, and data distribution.