# Benchmarking Secure Sampling Protocols for Differential Privacy

arXiv: https://arxiv.org/abs/2409.10667 · HTML: https://mlsanity.com/paper/2409.10667/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2024.

## Built on by (most cited first, 4 of 4)

- [A Robust Framework for Secure Cardiovascular Risk Prediction: An Architectural Case Study of Differentially Private Federated Learning](https://mlsanity.com/paper/2603.13293/) (2026)
- [A Comparative Benchmark of Federated Learning Strategies for Mortality Prediction on Heterogeneous and Imbalanced Clinical Data](https://mlsanity.com/paper/2509.10517/) (2025)
- [SoK: Enhancing Cryptographic Collaborative Learning with Differential Privacy](https://mlsanity.com/paper/2601.09460/) (2026)
- [Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation](https://mlsanity.com/paper/2604.20596/) (2026)

## Abstract (arXiv)

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.