Subsampled Renyi Differential Privacy and Analytical Moments Accountant
Abstract
We study the problem of subsampling in differential privacy (DP), a question that is the centerpiece behind many successful differentially private machine learning algorithms. Specifically, we provide a tight upper bound on the Renyi Differential Privacy (RDP) [Mironov 2017] parameters for algorithms that: (1) subsample the dataset, and then (2) applies a randomized mechanism M to the subsample, in terms of the RDP parameters of M and the subsampling probability parameter. Our results generalize the moments accounting technique, developed by [Abadi et al. 2016] for the Gaussian mechanism, to any subsampled RDP mechanism.
Cite
Text
Wang et al. "Subsampled Renyi Differential Privacy and Analytical Moments Accountant." Artificial Intelligence and Statistics, 2019.Markdown
[Wang et al. "Subsampled Renyi Differential Privacy and Analytical Moments Accountant." Artificial Intelligence and Statistics, 2019.](https://mlanthology.org/aistats/2019/wang2019aistats-subsampled/)BibTeX
@inproceedings{wang2019aistats-subsampled,
title = {{Subsampled Renyi Differential Privacy and Analytical Moments Accountant}},
author = {Wang, Yu-Xiang and Balle, Borja and Kasiviswanathan, Shiva Prasad},
booktitle = {Artificial Intelligence and Statistics},
year = {2019},
pages = {1226-1235},
volume = {89},
url = {https://mlanthology.org/aistats/2019/wang2019aistats-subsampled/}
}