Private Machine Learning via Randomised Response

David Barber

Private Machine Learning via Randomised Response

Machine Learning 2020-02-26 v2 Machine Learning

Authors: David Barber

Abstract

We introduce a general learning framework for private machine learning based on randomised response. Our assumption is that all actors are potentially adversarial and as such we trust only to release a single noisy version of an individual's datapoint. We discuss a general approach that forms a consistent way to estimate the true underlying machine learning model and demonstrate this in the case of logistic regression.

Keywords

machine learning theory differential privacy machine learning

Cite

@article{arxiv.2001.04942,
  title  = {Private Machine Learning via Randomised Response},
  author = {David Barber},
  journal= {arXiv preprint arXiv:2001.04942},
  year   = {2020}
}

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