How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy. (arXiv:2303.00654v1 [cs.LG])

ML models are ubiquitous in real world applications and are a constant focus
of research. At the same time, the community has started to realize the
importance of protecting the privacy of ML training data.

Differential Privacy (DP) has become a gold standard for making formal
statements about data anonymization. However, while some adoption of DP has
happened in industry, attempts to apply DP to real world complex ML models are
still few and far between. The adoption of DP …

adoption applications community data differential privacy focus guide industry machine machine learning making ml models privacy protecting research standard training world