4 papers
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…
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…