Continual Release Moment Estimation with Differential Privacy
Abstract
We propose *Joint Moment Estimation* (JME), a method for continually and privately estimating both the first and second moments of a data stream with reduced noise compared to naive approaches. JME supports the *matrix mechanism* and exploits a joint sensitivity analysis to identify a privacy regime in which the second-moment estimation incurs no additional privacy cost, thereby improving accuracy while maintaining privacy. We demonstrate JME’s effectiveness in two applications: estimating the running mean and covariance matrix for Gaussian density estimation and model training with DP-Adam.
Cite
Text
Kalinin et al. "Continual Release Moment Estimation with Differential Privacy." Advances in Neural Information Processing Systems, 2025.Markdown
[Kalinin et al. "Continual Release Moment Estimation with Differential Privacy." Advances in Neural Information Processing Systems, 2025.](https://mlanthology.org/neurips/2025/kalinin2025neurips-continual/)BibTeX
@inproceedings{kalinin2025neurips-continual,
title = {{Continual Release Moment Estimation with Differential Privacy}},
author = {Kalinin, Nikita and Upadhyay, Jalaj and Lampert, Christoph H.},
booktitle = {Advances in Neural Information Processing Systems},
year = {2025},
url = {https://mlanthology.org/neurips/2025/kalinin2025neurips-continual/}
}