2 docs tagged with "DP-SGD"

DP fine-tuning: differential privacy bounds any single sample's influence on me — but ε isn't zero, and it isn't zero leakage

In one sentence clip per-sample gradients + add noise) gives a provable property ε is not zero — it's "bounded leakage," not "zero leakage"; and more private usually means lower utility. Don't read "DP was applied" as "private" — look at what ε is, and whether it protects a sample or a user.

DP privacy auditing: is the ε you claim real? Empirically lower-bound your DP guarantee in one training run

In one sentence insert many independent probes into training, then use how well you can guess "which probes were trained on" to derive an empirical ε lower bound — the privacy you actually deliver is no stronger than that bound. Steinke et al. (NeurIPS 2023 Outstanding Paper) made it runnable in one training run, cheap enough to keep as a regression check. Conclusion first: "we used a DP library" does not mean "the ε holds", and auditing is the only empirical way to turn a claimed ε into an audited ε.