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arXiv:2001.04942cs.LG

Private Machine Learning via Randomised Response

David Barber

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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.

arXiv comments
Journal reference
DOI
Subjects
cs.LGMachine Learning stat.MLMachine Learning (Statistics)

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  1. A better way to build ML: Why you should be using Active Learning 1 comment

    • TL;DR I think its complementary. Synthetic data is particularly valuable when even the unlabelled data is expensive to obtain. For example if you want to train a driverless car, you may never see an ambulance driving at night in the rain even if you drive for thousands of miles.…

      razcleView on HNParent context

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