Renyi Differential Privacy of Propose-Test-Release and Applications to Private and Robust Machine Learning. (arXiv:2209.07716v1 [cs.CR])

Propose-Test-Release (PTR) is a differential privacy framework that works
with local sensitivity of functions, instead of their global sensitivity. This
framework is typically used for releasing robust statistics such as median or
trimmed mean in a differentially private manner. While PTR is a common
framework introduced over a decade ago, using it in applications such as robust
SGD where we need many adaptive robust queries is challenging. This is mainly
due to the lack of Renyi Differential Privacy (RDP) analysis, …

applications differential privacy machine machine learning privacy release test