1 citations · 2 across the 6 of their papers we have counts for
6 papers
Near-Optimal Machine Unlearning Utility for Smooth Strongly Convex Losses
Matthew Regehr, Gautam Kamath, Andrew Lowy
Machine unlearning is motivated by legal and user-facing requirements to remove the influence of individuals' data from trained models, such as the right to be forgotten. Prior wor…
Optimal Rates for Pure -Differentially Private Stochastic Convex Optimization with Heavy Tails
Andrew Lowy
We study stochastic convex optimization (SCO) with heavy-tailed gradients under pure -differential privacy (DP). Instead of assuming a bound on the worst-case Lipschit…
Exploring User-level Gradient Inversion with a Diffusion Prior
Zhuohang Li, Andrew Lowy, Jing Liu +4
We explore user-level gradient inversion as a new attack surface in distributed learning. We first investigate existing attacks on their ability to make inferences about private in…
Analyzing Inference Privacy Risks Through Gradients in Machine Learning
Zhuohang Li, Andrew Lowy, Jing Liu +4
In distributed learning settings, models are iteratively updated with shared gradients computed from potentially sensitive user data. While previous work has studied various privac…
Efficient Differentially Private Fine-Tuning of Diffusion Models
Jing Liu, Andrew Lowy, Toshiaki Koike-Akino +2
The recent developments of Diffusion Models (DMs) enable generation of astonishingly high-quality synthetic samples. Recent work showed that the synthetic samples generated by the…
Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks?
Andrew Lowy, Zhuohang Li, Jing Liu +3
For small privacy parameter , -differential privacy (DP) provides a strong worst-case guarantee that no membership inference attack (MIA) can succeed at determining whether a…