Decentralized learning is appealing as it enables the scalable usage of large
amounts of distributed data and resources (without resorting to any central
entity), while promoting privacy since every user minimizes the direct exposure
of their data. Yet, without additional precautions, curious users can still
leverage models obtained from their peers to violate privacy. In this paper, we
propose Decor, a variant of decentralized SGD with differential privacy (DP)
guarantees. Essentially, in Decor, users securely exchange randomness seeds in
one communication round to generate pairwise-canceling correlated Gaussian
noises, which are injected to protect local models at every communication
round. We theoretically and empirically show that, for arbitrary connected
graphs, Decor matches the central DP optimal privacy-utility trade-off. We do
so under SecLDP, our new relaxation of local DP, which protects all user
communications against an external eavesdropper and curious users, assuming
that every pair of connected users shares a secret, i.e., an information hidden
to all others. The main theoretical challenge is to control the accumulation of
non-canceling correlated noise due to network sparsity. We also propose a
companion SecLDP privacy accountant for public use.

本文提出了 Decor，它是一种具有差分隐私保证的分散式 SGD 变体，通过一轮通信中安全地交换随机种子来生成成对抵消的相关高斯噪声，以保护每一轮通信中的本地模型，并在理论上和实践上证明 Decor 在任意连通图中匹配了中心化差分隐私的最佳隐私 - 效用折衷，并在 SecLDP 下实现了所有用户通信的保护，假设每对连接的用户共享一个密钥作为隐藏信息。