# Differentially Private Ensemble Classifiers for Data Streams

arXiv: https://arxiv.org/abs/2112.04640 · HTML: https://mlsanity.com/paper/2112.04640/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 46 references that are in the map.
- First posted on arXiv 9 Dec 2021.

## Builds on (most cited first, 5 of 5)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models](https://mlsanity.com/paper/1804.04637/) (2018)
- [Scikit-Multiflow: A Multi-output Streaming Framework](https://mlsanity.com/paper/1807.04662/) (2018)
- [A Primer on Private Statistics](https://mlsanity.com/paper/2005.00010/) (2020)

## Built on by (most cited first, 1 of 1)

- [Unraveling Privacy Risks of Individual Fairness in Graph Neural Networks](https://mlsanity.com/paper/2301.12951/) (2023)

## Abstract (arXiv)

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 textitunbounded number of ensemble updates to deal with the potentially never-ending data streams under a fixed privacy budget, and it is textitmodel 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.