# Exact Privacy Analysis of the Gaussian Sparse Histogram Mechanism

arXiv: https://arxiv.org/abs/2202.01100 · HTML: https://mlsanity.com/paper/2202.01100/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2022.

## Built on by (most cited first, 6 of 6)

- [Continual Release Moment Estimation with Differential Privacy](https://mlsanity.com/paper/2502.06597/) (2025)
- [The Correlated Gaussian Sparse Histogram Mechanism](https://mlsanity.com/paper/2412.10357/) (2024)
- [PAPAYA Federated Analytics Stack: Engineering Privacy, Scalability and Practicality](https://mlsanity.com/paper/2412.02340/) (2024)
- [Private Rate-Constrained Optimization with Applications to Fair Learning](https://mlsanity.com/paper/2505.22703/) (2025)
- [DPSQL+: A Differentially Private SQL Library with a Minimum Frequency Rule](https://mlsanity.com/paper/2602.22699/) (2026)
- [Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees](https://mlsanity.com/paper/2605.22756/) (2026)

## Abstract (arXiv)

Sparse histogram methods can be useful for returning differentially private counts of items in large or infinite histograms, large group-by queries, and more generally, releasing a set of statistics with sufficient item counts. We consider the Gaussian version of the sparse histogram mechanism and study the exact ε,δ differential privacy guarantees satisfied by this mechanism. We compare these exact ε,δ parameters to the simpler overestimates used in prior work to quantify the impact of their looser privacy bounds.