In decentralized networks, nodes cannot ensure that their shared information will be securely preserved by their neighbors, making privacy vulnerable to inference by curious nodes. Adding calibrated random noise before communication to satisfy differential privacy offers a proven defense; however, most existing methods are tailored to specific downstream tasks and lack a general, protocol-level privacy-preserving solution. To bridge this gap, we propose Differentially Private Perturbed Push-Sum (DPPS), a lightweight differential privacy protocol for decentralized communication. Since protocol-level differential privacy introduces the unique challenge of obtaining the sensitivity for each communication round, DPPS introduces a novel sensitivity estimation mechanism that requires each node to compute and broadcast only one scalar per round, enabling rigorous differential privacy guarantees. This design allows DPPS to serve as a plug-and-play, low-cost privacy-preserving solution for downstream applications built on it. To provide a concrete instantiation of DPPS and better balance the privacy-utility trade-off, we design PartPSP, a privacy-preserving decentralized algorithm for non-convex optimization that integrates a partial communication mechanism. By partitioning model parameters into local and shared components and applying DPPS only to the shared parameters, PartPSP reduces the dimensionality of consensus data, thereby lowering the magnitude of injected noise and improving optimization performance. We theoretically prove that PartPSP converges under non-convex objectives and, with partial communication, achieves better optimization performance under the same privacy budget. Experimental results validate the effectiveness of DPPS's privacy-preserving and demonstrate that PartPSP outperforms existing privacy-preserving decentralized optimization algorithms.

翻译：在去中心化网络中，节点无法确保其共享信息能被邻居安全保存，使得隐私容易受到好奇节点的推断攻击。通过在通信前添加经过校准的随机噪声以满足差分隐私要求，已被证明是一种有效的防御手段；然而，现有方法大多针对特定下游任务设计，缺乏通用、协议级的隐私保护解决方案。为填补这一空白，我们提出了差分隐私扰动推送和（DPPS），一种用于去中心化通信的轻量级差分隐私协议。由于协议级差分隐私带来了获取每轮通信敏感度的独特挑战，DPPS引入了一种新颖的敏感度估计机制，该机制仅要求每个节点每轮计算并广播一个标量，从而能够提供严格的差分隐私保证。这一设计使得DPPS能够作为即插即用、低成本的隐私保护解决方案，服务于基于该协议构建的下游应用。为提供DPPS的具体实例并更好地平衡隐私与效用的权衡，我们设计了PartPSP——一种集成部分通信机制的隐私保护去中心化非凸优化算法。通过将模型参数划分为局部组件和共享组件，并仅对共享参数应用DPPS，PartPSP降低了共识数据的维度，从而减少了注入噪声的幅度并提升了优化性能。我们从理论上证明了PartPSP在非凸目标函数下的收敛性，并且通过部分通信机制，在相同隐私预算下实现了更优的优化性能。实验结果验证了DPPS隐私保护的有效性，并表明PartPSP在性能上优于现有的隐私保护去中心化优化算法。