Saved in:
| Main Authors: | Campbell, Andrew, Scaglione, Anna, Peisert, Sean |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.22849 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Differentially Private Distribution Release of Gaussian Mixture Models via KL-Divergence Minimization
by: Liu, Hang, et al.
Published: (2025)
by: Liu, Hang, et al.
Published: (2025)
Differentially Private Synthetic Voltage Phasor Release for Distribution Grids
by: Campbell, Andrew, et al.
Published: (2026)
by: Campbell, Andrew, et al.
Published: (2026)
Differentially Private Communication of Measurement Anomalies in the Smart Grid
by: Ravi, Nikhil, et al.
Published: (2024)
by: Ravi, Nikhil, et al.
Published: (2024)
Differential Privacy of Network Parameters from a System Identification Perspective
by: Campbell, Andrew, et al.
Published: (2025)
by: Campbell, Andrew, et al.
Published: (2025)
Graph Transfer Learning via Shared Latent Geometry: Theory and Applications
by: Wu, Tong, et al.
Published: (2026)
by: Wu, Tong, et al.
Published: (2026)
Optimum Noise Mechanism for Differentially Private Queries in Discrete Finite Sets
by: Kadam, Sachin, et al.
Published: (2021)
by: Kadam, Sachin, et al.
Published: (2021)
Compressed and Sparse Models for Non-Convex Decentralized Learning
by: Campbell, Andrew, et al.
Published: (2023)
by: Campbell, Andrew, et al.
Published: (2023)
Complex-Value Spatio-temporal Graph Convolutional Neural Networks and its Applications to Electric Power Systems AI
by: Wu, Tong, et al.
Published: (2022)
by: Wu, Tong, et al.
Published: (2022)
Differentially Private Decentralized Learning with Random Walks
by: Cyffers, Edwige, et al.
Published: (2024)
by: Cyffers, Edwige, et al.
Published: (2024)
Adaptive Power Iteration Method for Differentially Private PCA
by: Nguyen, Ta Duy, et al.
Published: (2026)
by: Nguyen, Ta Duy, et al.
Published: (2026)
Straggler-Resilient Differentially-Private Decentralized Learning
by: Yakimenka, Yauhen, et al.
Published: (2022)
by: Yakimenka, Yauhen, et al.
Published: (2022)
Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise
by: Saha, Utsab, et al.
Published: (2025)
by: Saha, Utsab, et al.
Published: (2025)
Shuffled Linear Regression via Spectral Matching
by: Liu, Hang, et al.
Published: (2024)
by: Liu, Hang, et al.
Published: (2024)
Generalizing Differentially Private Decentralized Deep Learning with Multi-Agent Consensus
by: Bayrooti, Jasmine, et al.
Published: (2023)
by: Bayrooti, Jasmine, et al.
Published: (2023)
A Stochastic Optimization Framework for Private and Fair Learning From Decentralized Data
by: Gupta, Devansh, et al.
Published: (2024)
by: Gupta, Devansh, et al.
Published: (2024)
Dropout-Robust Mechanisms for Differentially Private and Fully Decentralized Mean Estimation
by: Sabater, César, et al.
Published: (2025)
by: Sabater, César, et al.
Published: (2025)
Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee
by: Zhu, Zehan, et al.
Published: (2025)
by: Zhu, Zehan, et al.
Published: (2025)
Enhancing Privacy in Decentralized Min-Max Optimization: A Differentially Private Approach
by: Quan, Yueyang, et al.
Published: (2025)
by: Quan, Yueyang, et al.
Published: (2025)
Efficient Node Selection in Private Personalized Decentralized Learning
by: Zec, Edvin Listo, et al.
Published: (2023)
by: Zec, Edvin Listo, et al.
Published: (2023)
Output Perturbation for Differentially Private Convex Optimization: Faster and More General
by: Lowy, Andrew, et al.
Published: (2021)
by: Lowy, Andrew, et al.
Published: (2021)
Differentially Private Wasserstein Barycenters
by: Gu, Anming, et al.
Published: (2025)
by: Gu, Anming, et al.
Published: (2025)
Differentially Private Policy Gradient
by: Rio, Alexandre, et al.
Published: (2025)
by: Rio, Alexandre, et al.
Published: (2025)
Differentially Private Geodesic Regression
by: Kulkarni, Aditya, et al.
Published: (2025)
by: Kulkarni, Aditya, et al.
Published: (2025)
Differentially Private Conformal Prediction
by: Wu, Jiamei, et al.
Published: (2026)
by: Wu, Jiamei, et al.
Published: (2026)
Efficient Differentially Private Fine-Tuning of Diffusion Models
by: Liu, Jing, et al.
Published: (2024)
by: Liu, Jing, et al.
Published: (2024)
Differentially Private Permutation Tests: Applications to Kernel Methods
by: Kim, Ilmun, et al.
Published: (2023)
by: Kim, Ilmun, et al.
Published: (2023)
Optimal Rates for Pure $\varepsilon$-Differentially Private Stochastic Convex Optimization with Heavy Tails
by: Lowy, Andrew
Published: (2026)
by: Lowy, Andrew
Published: (2026)
Almost Sure Convergence Analysis of Differentially Private Stochastic Gradient Methods
by: Mukherjee, Amartya, et al.
Published: (2025)
by: Mukherjee, Amartya, et al.
Published: (2025)
How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization
by: Lowy, Andrew, et al.
Published: (2024)
by: Lowy, Andrew, et al.
Published: (2024)
Optimal Differentially Private Model Training with Public Data
by: Lowy, Andrew, et al.
Published: (2023)
by: Lowy, Andrew, et al.
Published: (2023)
Differentially Private Kernelized Contextual Bandits
by: Pavlovic, Nikola, et al.
Published: (2025)
by: Pavlovic, Nikola, et al.
Published: (2025)
Locally Differentially Private Thresholding Bandits
by: Barbara, Annalisa, et al.
Published: (2025)
by: Barbara, Annalisa, et al.
Published: (2025)
Differentially Private Bilevel Optimization: Efficient Algorithms with Near-Optimal Rates
by: Lowy, Andrew, et al.
Published: (2025)
by: Lowy, Andrew, et al.
Published: (2025)
Delving into Differentially Private Transformer
by: Ding, Youlong, et al.
Published: (2024)
by: Ding, Youlong, et al.
Published: (2024)
Differentially Private Attention Computation
by: Gao, Yeqi, et al.
Published: (2023)
by: Gao, Yeqi, et al.
Published: (2023)
Differentially Private Diffusion Models
by: Dockhorn, Tim, et al.
Published: (2022)
by: Dockhorn, Tim, et al.
Published: (2022)
Private and Fair Machine Learning: Revisiting the Disparate Impact of Differentially Private SGD
by: Demelius, Lea, et al.
Published: (2025)
by: Demelius, Lea, et al.
Published: (2025)
Privately Learning Decision Lists and a Differentially Private Winnow
by: Bun, Mark, et al.
Published: (2026)
by: Bun, Mark, et al.
Published: (2026)
Model Agnostic Differentially Private Causal Inference
by: Lebeda, Christian Janos, et al.
Published: (2025)
by: Lebeda, Christian Janos, et al.
Published: (2025)
Online Sensitivity Optimization in Differentially Private Learning
by: Galli, Filippo, et al.
Published: (2023)
by: Galli, Filippo, et al.
Published: (2023)
Similar Items
-
Differentially Private Distribution Release of Gaussian Mixture Models via KL-Divergence Minimization
by: Liu, Hang, et al.
Published: (2025) -
Differentially Private Synthetic Voltage Phasor Release for Distribution Grids
by: Campbell, Andrew, et al.
Published: (2026) -
Differentially Private Communication of Measurement Anomalies in the Smart Grid
by: Ravi, Nikhil, et al.
Published: (2024) -
Differential Privacy of Network Parameters from a System Identification Perspective
by: Campbell, Andrew, et al.
Published: (2025) -
Graph Transfer Learning via Shared Latent Geometry: Theory and Applications
by: Wu, Tong, et al.
Published: (2026)