NeurIPS 2025poster0 citations
Continual Release Moment Estimation with Differential Privacy
Nikita Kalinin, Jalaj Upadhyay, Christoph H. Lampert
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.
Differential PrivacyContinual ReleaseMoment Estimation
BibTeX
@inproceedings{
kalinin2025continual,
title={Continual Release Moment Estimation with Differential Privacy},
author={Nikita Kalinin and Jalaj Upadhyay and Christoph H. Lampert},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2025},
url={https://openreview.net/forum?id=TXHc1gEEIk}
}