Differentially Private Ensemble Classifiers for Data Streams
This addresses privacy-preserving machine learning for data streams, an incremental improvement in handling unbounded updates under fixed privacy budgets.
The paper tackled the challenge of adapting to concept drift in continuous data streams while protecting data privacy, presenting a differentially private ensemble solution that outperformed competitors on real-world and simulated datasets.
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 \textit{unbounded} number of ensemble updates to deal with the potentially never-ending data streams under a fixed privacy budget, and it is \textit{model 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.