Summary of Privacy-preserving Low-rank Adaptation Against Membership Inference Attacks For Latent Diffusion Models, by Zihao Luo et al.
Privacy-Preserving Low-Rank Adaptation against Membership Inference Attacks for Latent Diffusion Models
by Zihao Luo, Xilie Xu, Feng Liu, Yun Sing Koh, Di Wang, Jingfeng Zhang
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
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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 Low-rank adaptation (LoRA) is a strategy for adapting latent diffusion models (LDMs) on private datasets to generate specific images. However, LoRA-adapted LDMs are vulnerable to membership inference (MI) attacks that can infer whether a data point belongs to the private dataset, compromising privacy. To defend against MI attacks, the authors propose Membership-Privacy-preserving LoRA (MP-LoRA), which is formulated as a min-max optimization problem. While MP-LoRA shows promise, it has an issue with unstable optimization due to unconstrained local smoothness, which impedes privacy preservation. The authors then introduce Stable Membership-Privacy-preserving LoRA (SMP-LoRA) that adapts the LDM by minimizing the ratio of adaptation loss to MI gain. SMP-LoRA is theoretically proven to have constrained local smoothness, leading to improved convergence and successful defense against MI attacks. Experimental results demonstrate that SMP-LoRA generates high-quality images while preserving privacy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to make a computer program generate pictures of specific things, like dogs or cats. To do this, you’d need to adapt the program’s settings based on some private data. However, if someone tried to figure out which data points came from your private dataset, that would be a problem for privacy. To solve this issue, researchers propose a new method called Stable Membership-Privacy-preserving LoRA (SMP-LoRA). This method adjusts the program’s settings in a way that balances generating good pictures with keeping your data private. The results show that SMP-LoRA can successfully generate high-quality images while protecting privacy. |
Keywords
* Artificial intelligence * Inference * Lora * Low rank adaptation * Optimization
