Gradient-Guided Conditional Diffusion Models for Private Image Reconstruction: Analyzing Adversarial Impacts of Differential Privacy and Denoising
Fuente:
arXiv
Saved in:
| Main Authors: | Huang, Tao, Meng, Jiayang, Chen, Hong, Zheng, Guolong, Yang, Xu, Yi, Xun, Wang, Hua |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Enhanced Privacy Leakage from Noise-Perturbed Gradients via Gradient-Guided Conditional Diffusion Models
by: Meng, Jiayang, et al.
Published: (2025)
by: Meng, Jiayang, et al.
Published: (2025)
Mitigating Membership Inference in Intermediate Representations with Differentially Private Training
by: Meng, Jiayang, et al.
Published: (2026)
by: Meng, Jiayang, et al.
Published: (2026)
DP-aware AdaLN-Zero: Taming Conditioning-Induced Heavy-Tailed Gradients in Differentially Private Diffusion
by: Huang, Tao, et al.
Published: (2026)
by: Huang, Tao, et al.
Published: (2026)
Enhancing DP-SGD through Non-monotonous Adaptive Scaling Gradient Weight
by: Huang, Tao, et al.
Published: (2024)
by: Huang, Tao, et al.
Published: (2024)
Is Diffusion Model Safe? Severe Data Leakage via Gradient-Guided Diffusion Model
by: Meng, Jiayang, et al.
Published: (2024)
by: Meng, Jiayang, et al.
Published: (2024)
Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage
by: Meng, Jiayang, et al.
Published: (2025)
by: Meng, Jiayang, et al.
Published: (2025)
Machine Unlearning with Minimal Gradient Dependence for High Unlearning Ratios
by: Huang, Tao, et al.
Published: (2024)
by: Huang, Tao, et al.
Published: (2024)
Adaptive Diffusion Denoised Smoothing : Certified Robustness via Randomized Smoothing with Differentially Private Guided Denoising Diffusion
by: Shpilevskiy, Frederick, et al.
Published: (2025)
by: Shpilevskiy, Frederick, et al.
Published: (2025)
DPDR: Gradient Decomposition and Reconstruction for Differentially Private Deep Learning
by: Liu, Yixuan, et al.
Published: (2024)
by: Liu, Yixuan, et al.
Published: (2024)
Radio-astronomical Image Reconstruction with Conditional Denoising Diffusion Model
by: Drozdova, Mariia, et al.
Published: (2024)
by: Drozdova, Mariia, et al.
Published: (2024)
Beyond Image Prior: Embedding Noise Prior into Conditional Denoising Transformer
by: Huang, Yuanfei, et al.
Published: (2024)
by: Huang, Yuanfei, et al.
Published: (2024)
Adversarial Signed Graph Learning with Differential Privacy
by: Ke, Haobin, et al.
Published: (2025)
by: Ke, Haobin, et al.
Published: (2025)
Differentially Private Manifold Denoising
by: Wu, Jiaqi, et al.
Published: (2026)
by: Wu, Jiaqi, et al.
Published: (2026)
Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent
by: Yu, Da, et al.
Published: (2022)
by: Yu, Da, et al.
Published: (2022)
Observable Channels, Not Just Storage: Evaluating Privacy Leakage in LLM Agent Pipelines
by: Huang, Tao, et al.
Published: (2026)
by: Huang, Tao, et al.
Published: (2026)
Tackling Privacy Heterogeneity in Differentially Private Federated Learning
by: Xu, Ruichen, et al.
Published: (2026)
by: Xu, Ruichen, et al.
Published: (2026)
Adversarial Analysis of the Differentially-Private Federated Learning in Cyber-Physical Critical Infrastructures
by: Hossain, Md Tamjid, et al.
Published: (2022)
by: Hossain, Md Tamjid, et al.
Published: (2022)
Private Linear Regression with Differential Privacy and PAC Privacy
by: Yang, Hillary, et al.
Published: (2024)
by: Yang, Hillary, et al.
Published: (2024)
DPSR: Differentially Private Sparse Reconstruction via Multi-Stage Denoising for Recommender Systems
by: Ali, Sarwan
Published: (2025)
by: Ali, Sarwan
Published: (2025)
A Convergent Generalized Krylov Subspace Method for Compressed Sensing MRI Reconstruction with Gradient-Driven Denoisers
by: Hong, Tao, et al.
Published: (2025)
by: Hong, Tao, et al.
Published: (2025)
Convergent Complex Quasi-Newton Proximal Methods for Gradient-Driven Denoisers in Compressed Sensing MRI Reconstruction
by: Hong, Tao, et al.
Published: (2025)
by: Hong, Tao, et al.
Published: (2025)
Physics-Guided Conditional Diffusion Networks for Microwave Image Reconstruction
by: Chehelgami, Shirin, et al.
Published: (2025)
by: Chehelgami, Shirin, et al.
Published: (2025)
Differentially Private Policy Gradient
by: Rio, Alexandre, et al.
Published: (2025)
by: Rio, Alexandre, et al.
Published: (2025)
Learning Differentially Private Diffusion Models via Stochastic Adversarial Distillation
by: Liu, Bochao, et al.
Published: (2024)
by: Liu, Bochao, et al.
Published: (2024)
DiffDenoise: Self-Supervised Medical Image Denoising with Conditional Diffusion Models
by: Demir, Basar, et al.
Published: (2025)
by: Demir, Basar, et al.
Published: (2025)
Intermediate Outputs Are More Sensitive Than You Think
by: Huang, Tao, et al.
Published: (2024)
by: Huang, Tao, et al.
Published: (2024)
Harnessing Sparsification in Federated Learning: A Secure, Efficient, and Differentially Private Realization
by: Xu, Shuangqing, et al.
Published: (2025)
by: Xu, Shuangqing, et al.
Published: (2025)
On the Fairness of Privacy Protection: Measuring and Mitigating the Disparity of Group Privacy Risks for Differentially Private Machine Learning
by: Yang, Zhi, et al.
Published: (2025)
by: Yang, Zhi, et al.
Published: (2025)
Joint Conditional Diffusion Model for Image Restoration with Mixed Degradations
by: Yue, Yufeng, et al.
Published: (2024)
by: Yue, Yufeng, et al.
Published: (2024)
Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data
by: Alsubaie, Mohammed, et al.
Published: (2025)
by: Alsubaie, Mohammed, et al.
Published: (2025)
Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition
by: Xu, Difei, et al.
Published: (2025)
by: Xu, Difei, et al.
Published: (2025)
PrivateXR: Defending Privacy Attacks in Extended Reality Through Explainable AI-Guided Differential Privacy
by: Kundu, Ripan Kumar, et al.
Published: (2025)
by: Kundu, Ripan Kumar, et al.
Published: (2025)
Fragile Reconstruction: Adversarial Vulnerability of Reconstruction-Based Detectors for Diffusion-Generated Images
by: Jiang, Haoyang, et al.
Published: (2026)
by: Jiang, Haoyang, et al.
Published: (2026)
Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy
by: Lin, Yingyu, et al.
Published: (2023)
by: Lin, Yingyu, et al.
Published: (2023)
Controllable Adversarial Makeup for Privacy via Text-Guided Diffusion
by: Kwon, Youngjin, et al.
Published: (2025)
by: Kwon, Youngjin, et al.
Published: (2025)
Adversarial Guided Diffusion Models for Adversarial Purification
by: Lin, Guang, et al.
Published: (2024)
by: Lin, Guang, et al.
Published: (2024)
Advances in Differential Privacy and Differentially Private Machine Learning
by: Das, Saswat, et al.
Published: (2024)
by: Das, Saswat, et al.
Published: (2024)
Temporal Residual Guided Diffusion Framework for Event-Driven Video Reconstruction
by: Zhu, Lin, et al.
Published: (2024)
by: Zhu, Lin, et al.
Published: (2024)
Tighter Privacy Auditing of Differentially Private Stochastic Gradient Descent in the Hidden State Threat Model
by: Bhuekar, Apeksha
Published: (2026)
by: Bhuekar, Apeksha
Published: (2026)
DP-CSGP: Differentially Private Stochastic Gradient Push with Compressed Communication
by: Zhu, Zehan, et al.
Published: (2025)
by: Zhu, Zehan, et al.
Published: (2025)
Similar Items
-
Enhanced Privacy Leakage from Noise-Perturbed Gradients via Gradient-Guided Conditional Diffusion Models
by: Meng, Jiayang, et al.
Published: (2025) -
Mitigating Membership Inference in Intermediate Representations with Differentially Private Training
by: Meng, Jiayang, et al.
Published: (2026) -
DP-aware AdaLN-Zero: Taming Conditioning-Induced Heavy-Tailed Gradients in Differentially Private Diffusion
by: Huang, Tao, et al.
Published: (2026) -
Enhancing DP-SGD through Non-monotonous Adaptive Scaling Gradient Weight
by: Huang, Tao, et al.
Published: (2024) -
Is Diffusion Model Safe? Severe Data Leakage via Gradient-Guided Diffusion Model
by: Meng, Jiayang, et al.
Published: (2024)