论文标题
完全分散的数据集对私有线性回归
Differentially Private Linear Regression over Fully Decentralized Datasets
论文作者
论文摘要
本文介绍了一种以分散方式进行线性回归学习的差异私有算法。根据该算法，理论上是派生的隐私预算，除了解决方案错误被证明是由$ o（1/t）$ descent步骤尺寸和$ O（\ exp（t^{1-e}）$的$ O（1/t）$（t^{ - e}）$下降步骤大小的$ O（1/T）$（\ exp（t^{1-e}）$）的界限。
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.
