Summary of Datacook: Crafting Anti-adversarial Examples For Healthcare Data Copyright Protection, by Sihan Shang and Jiancheng Yang and Zhenglong Sun and Pascal Fua
DataCook: Crafting Anti-Adversarial Examples for Healthcare Data Copyright Protection
by Sihan Shang, Jiancheng Yang, Zhenglong Sun, Pascal Fua
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 The paper introduces DataCook, a novel approach to safeguard the copyright of healthcare data during deployment. It operates by “cooking” raw data before distribution, enabling normal model performance on processed data, while granting copyright holders control over authorization. The mechanism behind DataCook is crafting anti-adversarial examples (AntiAdv) that enhance model confidence. Extensive experiments on MedMNIST datasets show DataCook effectively prevents unauthorized data analysis without compromising validity and accuracy in legitimate scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DataCook helps keep healthcare data safe by modifying the way models are trained and tested. It “cooks” the raw data before sharing, making sure only authorized people can use it. This is different from regular methods that protect data beforehand. DataCook also creates special examples (AntiAdv) to make models more confident, rather than confused like usual examples do. The results show that DataCook works well and keeps unauthorized access out. |
