Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2022
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916335408119808 |
|---|---|
| author | Yu, Da Kamath, Gautam Kulkarni, Janardhan Liu, Tie-Yan Yin, Jian Zhang, Huishuai |
| author_facet | Yu, Da Kamath, Gautam Kulkarni, Janardhan Liu, Tie-Yan Yin, Jian Zhang, Huishuai |
| contents | Differentially private stochastic gradient descent (DP-SGD) is the workhorse algorithm for recent advances in private deep learning. It provides a single privacy guarantee to all datapoints in the dataset. We propose output-specific $(\varepsilon,δ)$-DP to characterize privacy guarantees for individual examples when releasing models trained by DP-SGD. We also design an efficient algorithm to investigate individual privacy across a number of datasets. We find that most examples enjoy stronger privacy guarantees than the worst-case bound. We further discover that the training loss and the privacy parameter of an example are well-correlated. This implies groups that are underserved in terms of model utility simultaneously experience weaker privacy guarantees. For example, on CIFAR-10, the average $\varepsilon$ of the class with the lowest test accuracy is 44.2\% higher than that of the class with the highest accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2206_02617 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent Yu, Da Kamath, Gautam Kulkarni, Janardhan Liu, Tie-Yan Yin, Jian Zhang, Huishuai Machine Learning Cryptography and Security Data Structures and Algorithms Differentially private stochastic gradient descent (DP-SGD) is the workhorse algorithm for recent advances in private deep learning. It provides a single privacy guarantee to all datapoints in the dataset. We propose output-specific $(\varepsilon,δ)$-DP to characterize privacy guarantees for individual examples when releasing models trained by DP-SGD. We also design an efficient algorithm to investigate individual privacy across a number of datasets. We find that most examples enjoy stronger privacy guarantees than the worst-case bound. We further discover that the training loss and the privacy parameter of an example are well-correlated. This implies groups that are underserved in terms of model utility simultaneously experience weaker privacy guarantees. For example, on CIFAR-10, the average $\varepsilon$ of the class with the lowest test accuracy is 44.2\% higher than that of the class with the highest accuracy. |
| title | Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent |
| topic | Machine Learning Cryptography and Security Data Structures and Algorithms |
| url | https://arxiv.org/abs/2206.02617 |