2000 character limit reached
Tight Group-Level DP Guarantees for DP-SGD with Sampling via Mixture of Gaussians Mechanisms (2401.10294v2)
Published 17 Jan 2024 in cs.CR and cs.LG
Abstract: We give a procedure for computing group-level $(\epsilon, \delta)$-DP guarantees for DP-SGD, when using Poisson sampling or fixed batch size sampling. Up to discretization errors in the implementation, the DP guarantees computed by this procedure are tight (assuming we release every intermediate iterate).
- Privacy of noisy stochastic gradient descent: More iterations without more privacy loss. arXiv preprint arXiv:2205.13710, 2022.
- (amplified) banded matrix factorization: A unified approach to private training. In Thirty-seventh Conference on Neural Information Processing Systems, 2023.
- Privacy amplification for matrix mechanisms, 2023.
- Differential privacy dynamics of langevin diffusion and noisy gradient descent. In Advances in Neural Information Processing Systems, 2021.
- Connect the dots: Tighter discrete approximations of privacy loss distributions, 2022.
- DP Team. Google’s differential privacy libraries., 2022. https://github.com/google/differential-privacy.
- Privacy amplification by iteration. In 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS), pages 521–532. IEEE, 2018.
- Numerical composition of differential privacy. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, volume 34, pages 11631–11642. Curran Associates, Inc., 2021.
- Tight differential privacy for discrete-valued mechanisms and for the subsampled gaussian mechanism using fft. In Arindam Banerjee and Kenji Fukumizu, editors, Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, volume 130 of Proceedings of Machine Learning Research, pages 3358–3366. PMLR, 13–15 Apr 2021.
- Salil Vadhan. The complexity of differential privacy. In Tutorials on the Foundations of Cryptography, pages 347–450. Springer, 2017.
- Optimal accounting of differential privacy via characteristic function. In Gustau Camps-Valls, Francisco J. R. Ruiz, and Isabel Valera, editors, Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, volume 151 of Proceedings of Machine Learning Research, pages 4782–4817. PMLR, 28–30 Mar 2022.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.
