2 citations · 2 across the 3 of their papers we have counts for
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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…
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…
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…
Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex Hulls
Saurav Prakash, Jin Sima, Chao Pan +2
Hierarchical and tree-like data sets arise in many applications, including language processing, graph data mining, phylogeny and genomics. It is known that tree-like data cannot be…
Differentially Private Decoupled Graph Convolutions for Multigranular Topology Protection
Eli Chien, Wei-Ning Chen, Chao Pan +3
GNNs can inadvertently expose sensitive user information and interactions through their model predictions. To address these privacy concerns, Differential Privacy (DP) protocols ar…