Classification under local differential privacy

Thomas Berrett; Cristina Butucea

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arXiv · 1912.04629

Classification under local differential privacy

Thomas Berrett·Cristina Butucea

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Abstract

We consider the binary classification problem in a setup that preserves the privacy of the original sample. We provide a privacy mechanism that is locally differentially private and then construct a classifier based on the private sample that is universally consistent in Euclidean spaces. Under stronger assumptions, we establish the minimax rates of convergence of the excess risk and see that they are slower than in the case when the original sample is available.

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Thomas Berrett, Cristina Butucea. 2019-12-10. Classification under local differential privacy. https://arxiv.org/abs/1912.04629

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