Differentially Private Ensemble Classifiers for Data Streams


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.

In Proceedings of the 15th ACM International Conference on Web Search and Data Mining
Lovedeep Gondara
Lovedeep Gondara
Research Scientist

My research interests include machine learning (deep learning) and statistics, with current research focus on large language models, differential privacy, and their applications to healthcare.