Differentially Private Ensemble Classifiers for Data Streams

Code

• github.com/lgondara/dptemporalensemble
  OfficialIn papernone★ 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 unbounded number of ensemble updates to deal with the potentially never-ending data streams under a fixed privacy budget, and it is 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.

Tasks

Reproductions