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arXiv · 2004.07425

Differentially Private Linear Regression over Fully Decentralized Datasets

Yang Liu·Xiong Zhang·Shuqi Qin·Xiaoping Lei
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Abstract

This paper presents a differentially private algorithm for linear regression learning in a decentralized fashion. Under this algorithm, privacy budget is theoretically derived, in addition to that the solution error is shown to be bounded by $O(t)$ for $O(1/t)$ descent step size and $O(\exp(t^{1-e}))$ for $O(t^{-e})$ descent step size.

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Yang Liu, Xiong Zhang, Shuqi Qin, Xiaoping Lei. 2020-04-16. Differentially Private Linear Regression over Fully Decentralized Datasets. https://arxiv.org/abs/2004.07425

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