Continual Release Moment Estimation with Differential Privacy
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910985153937408 |
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| author | Kalinin, Nikita P. Upadhyay, Jalaj Lampert, Christoph H. |
| author_facet | Kalinin, Nikita P. Upadhyay, Jalaj Lampert, Christoph H. |
| contents | We propose Joint Moment Estimation (JME), a method for continually and privately estimating both the first and second moments of data with reduced noise compared to naive approaches. JME uses the matrix mechanism and a joint sensitivity analysis to allow the second moment estimation with 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 on CIFAR-10. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_06597 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Continual Release Moment Estimation with Differential Privacy Kalinin, Nikita P. Upadhyay, Jalaj Lampert, Christoph H. Machine Learning We propose Joint Moment Estimation (JME), a method for continually and privately estimating both the first and second moments of data with reduced noise compared to naive approaches. JME uses the matrix mechanism and a joint sensitivity analysis to allow the second moment estimation with 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 on CIFAR-10. |
| title | Continual Release Moment Estimation with Differential Privacy |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.06597 |