# Tighter Privacy Analysis for Truncated Poisson Sampling

arXiv: https://arxiv.org/abs/2508.15089 · HTML: https://mlsanity.com/paper/2508.15089/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2025.

## Built on by (most cited first, 5 of 5)

- [JAX-Privacy: A library for differentially private machine learning](https://mlsanity.com/paper/2602.17861/) (2026)
- [Less Random, More Private: What is the Optimal Subsampling Scheme for DP-SGD?](https://mlsanity.com/paper/2605.07072/) (2026)
- [Correlating Cross-Iteration Noise for DP-SGD using Model Curvature](https://mlsanity.com/paper/2510.05416/) (2025)
- [ContinuousBench: Can Differentially Private Synthetic Text Improve Capabilities?](https://mlsanity.com/paper/2606.01849/) (2026)
- [Closed-Form Noise Calibration Against Membership Inference for Random-Allocation DP-SGD](https://mlsanity.com/paper/2610.09651/) (2026)

## Abstract (arXiv)

We give a new privacy amplification analysis for truncated Poisson sampling, a Poisson sampling variant that truncates a batch if it exceeds a given maximum batch size.