93 citations · 174 across the 25 of their papers we have counts for
28 papers
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…
Differentially Private Graph Diffusion with Applications in Personalized PageRanks
Rongzhe Wei, Eli Chien, Pan Li
Graph diffusion, which iteratively propagates real-valued substances among the graph, is used in numerous graph/network-involved applications. However, releasing diffusion vectors…
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…
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…
Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning
Zheyuan Liu, Guangyao Dou, Yijun Tian +3
Machine Unlearning (MU) algorithms have become increasingly critical due to the imperative adherence to data privacy regulations. The primary objective of MU is to erase the influe…
On the Inherent Privacy Properties of Discrete Denoising Diffusion Models
Rongzhe Wei, Eleonora Kreačić, 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…