# Plume: Differential Privacy at Scale

arXiv: https://arxiv.org/abs/2201.11603 · HTML: https://mlsanity.com/paper/2201.11603/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2022.

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

- [Mean estimation in the add-remove model of differential privacy](https://mlsanity.com/paper/2312.06658/) (2023)
- [Optimal Variance and Covariance Estimation under Differential Privacy in the Add-Remove Model and Beyond](https://mlsanity.com/paper/2509.04919/) (2025)
- [Nearly-Linear Time Private Hypothesis Selection with the Optimal Approximation Factor](https://mlsanity.com/paper/2506.01162/) (2025)
- [Differentially Private Synthetic Data Release for Topics API Outputs](https://mlsanity.com/paper/2506.23855/) (2025)
- [DPSQL+: A Differentially Private SQL Library with a Minimum Frequency Rule](https://mlsanity.com/paper/2602.22699/) (2026)

## Abstract (arXiv)

Differential privacy has become the standard for private data analysis, and an extensive literature now offers differentially private solutions to a wide variety of problems. However, translating these solutions into practical systems often requires confronting details that the literature ignores or abstracts away: users may contribute multiple records, the domain of possible records may be unknown, and the eventual system must scale to large volumes of data. Failure to carefully account for all three issues can severely impair a system's quality and usability. We present Plume, a system built to address these problems. We describe a number of sometimes subtle implementation issues and offer practical solutions that, together, make an industrial-scale system for differentially private data analysis possible. Plume is currently deployed at Google and is routinely used to process datasets with trillions of records.