mlsanityData to 7 Oct 2026

Gradient-Guided Conditional Diffusion Models for Private Image Reconstruction: Analyzing Adversarial Impacts of Differential Privacy and Denoising

arXiv 2411.03053 ↗First posted 5 Nov 2024cs.CV cs.AIcited by 1

On the map: Diffusion models → Robustness, privacy and adversarial aspects

Abstract (arXiv)

We investigate the construction of gradient-guided conditional diffusion models for reconstructing private images, focusing on the adversarial interplay between differential privacy noise and the denoising capabilities of diffusion models. While current gradient-based reconstruction methods struggle with high-resolution images due to computational complexity and prior knowledge requirements, we propose two novel methods that require minimal modifications to the diffusion model's generation process and eliminate the need for prior knowledge. Our approach leverages the strong image generation capabilities of diffusion models to reconstruct private images starting from randomly generated noise, even when a small amount of differentially private noise has been added to the gradients. We also conduct a comprehensive theoretical analysis of the impact of differential privacy noise on the quality of reconstructed images, revealing the relationship among noise magnitude, the architecture of attacked models, and the attacker's reconstruction capability. Additionally, extensive experiments validate the effectiveness of our proposed methods and the accuracy of our theoretical findings, suggesting new directions for privacy risk auditing using conditional diffusion models.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Robustness, privacy and adversarial aspects, one of 14 directions in Diffusion models. Builds on 31 of its 35 references that are in the map. First posted on arXiv 5 Nov 2024.

Builds on

31 of 35 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Is Diffusion Model Safe? Severe Data Leakage via Gradient-Guided Diffusion Model13 Jun

Other references · 30

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2020Denoising Diffusion Implicit Modelscited by 9,655
  4. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  5. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  6. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  7. 2022Classifier-Free Diffusion Guidancecited by 5,717
  8. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  9. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  10. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  11. 2016Deep Learning with Differential Privacycited by 2,682
  12. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
18 more
  1. 2022Diffusion Posterior Sampling for General Noisy Inverse Problemscited by 1,375
  2. 2019Deep Leakage from Gradientscited by 1,004
  3. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 707
  4. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 509
  5. 2022Improving Diffusion Models for Inverse Problems using Manifold Constraintscited by 501
  6. 2023Universal Guidance for Diffusion Modelscited by 426
  7. 2020iDLG: Improved Deep Leakage from Gradientscited by 367
  8. 2023Denoising Diffusion Models for Plug-and-Play Image Restorationcited by 335
  9. 2017Deep Models Under the GAN: Information Leakage from Collaborative Deep Learningcited by 326
  10. 2021See through Gradients: Image Batch Recovery via GradInversioncited by 320
  11. 2023FreeDoM: Training-Free Energy-Guided Conditional Diffusion Modelcited by 290
  12. 2023Manifold Preserving Guided Diffusioncited by 153
  13. 2021Gradient Inversion with Generative Image Priorcited by 101
  14. 2022Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakagecited by 72
  15. 2024Guidance with Spherical Gaussian Constraint for Conditional Diffusioncited by 69
  16. 2023Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllabilitycited by 66
  17. 2023GIFD: A Generative Gradient Inversion Method with Feature Domain Optimizationcited by 45
  18. 2022Concealing Sensitive Samples against Gradient Leakage in Federated Learningcited by 11

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakagecited by 0

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