Differential privacy (DP) is widely employed to provide privacy protection for individuals by limiting information leakage from the aggregated data. Two well-known models of DP are the central model and the local model. The former requires a trustworthy server for data aggregation, while the latter requires individuals to add noise, significantly decreasing the utility of aggregated results. Recently, many studies have proposed to achieve DP with Secure Multi-party Computation (MPC) in distributed settings, namely, the distributed model, which has utility comparable to central model while, under specific security assumptions, preventing parties from obtaining others' information. One challenge of realizing DP in distributed model is efficiently sampling noise with MPC. Although many secure sampling methods have been proposed, they have different security assumptions and isolated theoretical analyses. There is a lack of experimental evaluations to measure and compare their performances. We fill this gap by benchmarking existing sampling protocols in MPC and performing comprehensive measurements of their efficiency. First, we present a taxonomy of the underlying techniques of these sampling protocols. Second, we extend widely used distributed noise generation protocols to be resilient against Byzantine attackers. Third, we implement discrete sampling protocols and align their security settings for a fair comparison. We then conduct an extensive evaluation to study their efficiency and utility.

翻译：差分隐私（DP）被广泛用于通过限制聚合数据的信息泄露来为个体提供隐私保护。DP的两种经典模型是中心模型与本地模型：前者需要可信服务器进行数据聚合，后者则要求个体自行添加噪声，这会显著降低聚合结果的可用性。近年来，许多研究提出在分布式环境中通过安全多方计算（MPC）实现DP，即分布式模型，该模型在特定安全假设下既能防止参与方获取他人信息，又具有与中心模型相当的可用性。在分布式模型中实现DP的一个关键挑战是如何通过MPC高效地采样噪声。尽管已有多种安全采样方法被提出，但它们具有不同的安全假设和相互孤立的理论分析，目前仍缺乏衡量与比较其性能的实验评估。本研究通过为现有MPC采样协议建立性能基准并进行全面的效率测量来填补这一空白。首先，我们对这些采样协议的基础技术进行了分类学梳理。其次，我们扩展了广泛使用的分布式噪声生成协议，使其能够抵御拜占庭攻击者的攻击。第三，我们实现了离散采样协议，并统一其安全设置以确保公平比较。最后，我们通过大规模实验评估来研究这些协议的效率与可用性。