Differentially Private Ensemble Classifiers for Data Streams

• URL: http://arxiv.org/abs/2112.04640v1
• Date: Thu, 9 Dec 2021 00:55:04 GMT
• Title: Differentially Private Ensemble Classifiers for Data Streams
• Authors: Lovedeep Gondara, Ke Wang, Ricardo Silva Carvalho
• Abstract summary: 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 textitunbounded number of ensemble updates to deal with the potentially never-ending data streams. It is textitmodel agnostic, in that it treats any pre-trained differentially private classification/regression model as a black-box.
• Score: 3.9838304163788183
• License: http://creativecommons.org/licenses/by/4.0/
• 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 \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.

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