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arXiv · 2106.12576

DP-SGD vs PATE: Which Has Less Disparate Impact on Model Accuracy?

Archit Uniyal·Rakshit Naidu·Sasikanth Kotti·Sahib Singh·Patrik Joslin Kenfack·Fatemehsadat Mireshghallah·Andrew Trask
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Abstract

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.

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Archit Uniyal, Rakshit Naidu, Sasikanth Kotti, Sahib Singh, Patrik Joslin Kenfack, Fatemehsadat Mireshghallah, Andrew Trask. 2021-06-22. DP-SGD vs PATE: Which Has Less Disparate Impact on Model Accuracy?. https://arxiv.org/abs/2106.12576

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