Yang Liu, Xiong Zhang, Shuqi Qin et al.
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.
Yang Liu, Qingchen Liu, Xiong Zhang et al.
In this paper, we study the problem of summation evaluation of secrets. The secrets are distributed over a network of nodes that form a ring graph. Privacy-preserving iterative protocols for computing the sum of the secrets are proposed, which are compatible with node join and leave situations. Theoretic bounds are derived regarding the utility and accuracy, and the proposed protocols are shown to comply with differential privacy requirements. Based on utility, accuracy and privacy, we also provide guidance on appropriate selections of random noise parameters. Additionally, a few numerical examples that demonstrate their effectiveness are provided.