arXiv cs.AI· Sihan Shang, Jun Yan, Danli Shi, Haizhong Zheng, Shuangjia Zheng, Wei Zhao, Jiancheng Yang·· 3 小时前AI 评分35
DataCook：探索反对抗指纹用于医疗数据使用控制
DataCook: Exploring Anti-Adversarial Fingerprints for Healthcare Data Usage Control
AI 导读
研究者提出 DataCook，一种基于反对抗指纹的医疗数据部署阶段使用控制框架，通过对原始数据进行私有“烹饪”处理嵌入指纹。在 cooked 数据上训练的模型对同类输入保持强效用，而对原始输入性能下降，使下游模型效用依赖私有烹饪函数。实验覆盖 2D、3D 和高分辨率医学影像及结构化医疗表格数据，跨多种模型架构和自适应攻击设置验证了该机制的可行性。
正文 · 原文
Abstract:Purpose: Healthcare AI increasingly relies on high-quality datasets, yet medical data are scarce, costly to curate, and tightly regulated in terms of data access. More critically, once medical data are leaked or accessed without authorization, data owners have limited means to prevent such data from being used to train and deploy downstream models. To address this gap, we propose DataCook, a deployment-stage usage-control framework based on anti-adversarial fingerprints.
Materials and Methods: Specifically, DataCook transforms raw samples through a private cooking procedure that embeds anti-adversarial fingerprints. Models trained on the cooked data retain strong utility when evaluated on similarly cooked inputs, while their performance on raw inputs is reduced, making downstream model utility dependent on access to the private cooking function. We evaluate DataCook on 2D, 3D, and high-resolution medical images, as well as structured healthcare tabular data, across multiple model architectures and adaptive attack settings.
Results: Across the evaluated settings, DataCook generally preserves authorized model utility while reducing performance on unauthorized raw inputs. The authorization-dependent behavior is observed across multiple medical imaging settings, model architectures, and adaptive attack conditions, supporting the feasibility of the proposed deployment-stage usage-control mechanism.
Conclusion: These results demonstrate the feasibility of authorization-dependent model use at deployment, while the effectiveness of the current framework remains dependent on the specific task and evaluation metric.
| Comments: | 27 pages, 5 figures, 21 tables. Accepted for publication in Meta-Radiology. Substantially revised version with an updated title, author list, experimental evaluations, and appendices |
| Subjects: | Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2403.17755 [cs.AI] |
| (or arXiv:2403.17755v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2403.17755 arXiv-issued DOI via DataCite |
Submission history
From: Sihan Shang [view email]
[v1]
Tue, 26 Mar 2024 14:44:51 UTC (2,309 KB)
[v2]
Wed, 11 Nov 2026 04:31:09 UTC (1,739 KB)
来源：arXiv cs.AI · arxiv.org