4 papers · 1 filter
Langevin Unlearning: A New Perspective of Noisy Gradient Descent for Machine Unlearning
Eli Chien, Haoyu Wang, Ziang Chen +1
Machine unlearning has raised significant interest with the adoption of laws ensuring the ``right to be forgotten''. Researchers have provided a probabilistic notion of approximate…
Convergent Privacy Loss of Noisy-SGD without Convexity and Smoothness
Eli Chien, Pan Li
We study the Differential Privacy (DP) guarantee of hidden-state Noisy-SGD algorithms over a bounded domain. Standard privacy analysis for Noisy-SGD assumes all internal states are…
Certified Machine Unlearning via Noisy Stochastic Gradient Descent
Eli Chien, Haoyu Wang, Ziang Chen +1
``The right to be forgotten'' ensured by laws for user data privacy becomes increasingly important. Machine unlearning aims to efficiently remove the effect of certain data points…
On the Inherent Privacy Properties of Discrete Denoising Diffusion Models
Rongzhe Wei, Eleonora KreaÄiÄ, Haoyu Wang +4
Privacy concerns have led to a surge in the creation of synthetic datasets, with diffusion models emerging as a promising avenue. Although prior studies have performed empirical ev…