Recent advances in differentially private deep learning have demonstrated that application of differential privacy, specifically the DP-SGD algorithm, has a disparate impact on different sub-groups in the population, which leads to a significantly high drop-in model utility for sub-populations that are under-represented (minorities), compared to well-represented ones. In this work, we aim to compare PATE, another mechanism for training deep learning models using differential privacy, with DP-SGD in terms of fairness. We show that PATE does have a disparate impact too, however, it is much less severe than DP-SGD. We draw insights from this observation on what might be promising directions in achieving better fairness-privacy trade-offs.
https://arxiv.org/abs/2106.12576
@article{uniyal2021dp, title={DP-SGD vs PATE: Which Has Less Disparate Impact on Model Accuracy?}, author={Uniyal, Archit and Naidu, Rakshit and Kotti, Sasikanth and Singh, Sahib and Kenfack, Patrik Joslin and Mireshghallah, Fatemehsadat and Trask, Andrew}, journal={arXiv preprint arXiv:2106.12576}, year={2021} }