Summary of Enhancing Privacy in Controlnet and Stable Diffusion Via Split Learning, by Dixi Yao
Enhancing Privacy in ControlNet and Stable Diffusion via Split Learning
by Dixi Yao
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 The paper introduces ControlNet, a framework enabling fine-tuning of pre-trained generative models using user-specific data. To address concerns about data privacy in distributed training, it proposes a new learning structure that eliminates server-to-client gradient transmission. The authors evaluate existing attacks on split learning and find most ineffective except for two previously mentioned threats. To counter these risks, they develop a timestep sampling policy leveraging diffusion model properties, as well as a privacy-preserving activation function and method to prevent private prompts from leaving clients. Experimental results demonstrate the efficiency of this approach while preserving image generation quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about creating a special tool called ControlNet that helps people customize big models using their own data. The problem is: how can we train these models without sharing users’ personal information? The authors tried different methods and found one that works better than others. They also came up with new ways to keep user data safe, like changing the way they process information and preventing private prompts from being sent back to the server. Their tests show that this approach is fast and effective while still producing good results. |
Keywords
» Artificial intelligence » Diffusion model » Fine tuning » Image generation
