保护医疗保健中的隐私:对合成数据生成的深度学习方法的系统审查
Yintong Liu1, U Rajendra Acharya2, Jen Hong Tan3
1NUS-ISS, 25 Heng Mui Keng Terrace, Singapore, 119615, Singapore.
Computer methods and programs in biomedicine
|January 1, 2025
概括
使用深度学习的合成数据生成为医疗数据共享提供了一个保护隐私的解决方案. 像生成对抗网络 (GAN) 和变异自编码器 (VAE) 这样的技术显示出希望,尽管在偏见和监管对齐方面仍然存在挑战.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 人工智能的人工智能
- 数据 隐私 数据 隐私 数据
背景情况:
- 医疗保健数据共享对于研究和个性化医疗至关重要.
- 隐私,伦理和法律问题阻碍了传统的数据共享.
- 合成数据生成提供了一个增强隐私的替代方案.
研究的目的:
- 系统地审查医疗保健中合成数据生成的深度学习技术.
- 评估它们在维护数据实用性和隐私方面的有效性.
- 确定当前的挑战和未来的研究方向.
主要方法:
- 来自Scopus,科学网络,PubMed和IEEE (2019-2023) 的文献系统审查.
- 专注于深度学习模型:生成对抗网络 (GAN),变异自编码器 (VAE),扩散模型.
- 对数据相似性,实用性和隐私保护 (例如差异隐私,联合学习) 的指标的评估.
主要成果:
- GAN和VAE有效地在各种医疗保健数据类型中生成现实的合成数据.
- 提高隐私的方法,如差异隐私,可以降低重新识别的风险,同时保持数据的真实性.
- 挑战包括保持时间相关性,减少偏差,以及对复杂数据集的监管合规性.
结论:
- 合成数据生成是安全的医疗数据共享的一个有前途的方法.
- 对于先进的算法,强大的评估框架和质量保证,需要进一步的研究.
- 研究人员和政策制定者之间的合作对于制定明确的指导方针至关重要.
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