# Easy Differentially Private Linear Regression

@article{Amin2022EasyDP, title={Easy Differentially Private Linear Regression}, author={Kareem Amin and Matthew Joseph and M{\'o}nica Ribero and Sergei Vassilvitskii}, journal={ArXiv}, year={2022}, volume={abs/2208.07353} }

Linear regression is a fundamental tool for statistical analysis. This has motivated the development of linear regression methods that also satisfy diﬀerential privacy and thus guarantee that the learned model reveals little about any one data point used to construct it. However, existing diﬀerentially private solutions assume that the end user can easily specify good data bounds and hyperparameters. Both present signiﬁcant practical obstacles. In this paper, we study an algorithm which uses…

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