Differentially Private Ensemble Classifiers for Data Streams

To further add to the challenge, data streams from many domains involve sensitive, personal information about contributing Learning from continuous data streams via classification/regression users, such as patients' records and user data in mobile applications, is prevalent in many domains. Adapting to evolving data characteristics protection of which is of paramount interest. While concept (concept drift) while protecting data owners' private information drift and privacy have been extensively studied in isolation, works is an open challenge. We present a differentially private considering both are in infancy. See more discussion in Section ensemble solution to this problem with two distinguishing features: 2. In this work, our goal is to allow machine learning models to it allows an unbounded number of ensemble updates to deal with deal with concept drift when training on potentially never-ending the potentially never-ending data streams under a fixed privacy data streams involving sensitive data, where the model(s) learned budget, and it is model agnostic, in that it treats any pre-trained can be published without disclosing sensitive information.