Summary of Exploring User-level Gradient Inversion with a Diffusion Prior, by Zhuohang Li et al.
Exploring User-level Gradient Inversion with a Diffusion Prior
by Zhuohang Li, Andrew Lowy, Jing Liu, Toshiaki Koike-Akino, Bradley Malin, Kieran Parsons, Ye Wang
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary We investigate a new attack vector in distributed learning called user-level gradient inversion, which allows attackers to infer private information beyond training data reconstruction. Existing attacks fail to achieve good reconstruction quality, particularly in large batch and image sizes. To address this limitation, we propose a novel approach that uses a denoising diffusion model as a strong image prior to enhance recovery. Unlike traditional attacks, our method focuses on recovering representative images that capture sensitive shared semantic information corresponding to the underlying user. Our experiments with face images demonstrate the ability of our methods to recover realistic facial images along with private user attributes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to steal someone’s secret identity from a computer program. Normally, attacks like this try to reconstruct individual pieces of information, but they often fail when there are many people involved or the data is very large. We came up with a new way to attack that focuses on finding patterns in what makes each person unique. Our method uses a special kind of math problem to make educated guesses about someone’s identity. In our tests, we were able to successfully guess facial features and private information. |
Keywords
» Artificial intelligence » Diffusion model
