移动医疗系统需要采用隐私设计方法：评论文“医学研究中的联邦机器学习、隐私增强技术和数据保护法：范围综述”。
mHealth Systems Need a Privacy-by-Design Approach: Commentary on "Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review".
机构信息
Department of Statistics, University of Michigan, Ann Arbor, MI, United States.
Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States.
出版信息
J Med Internet Res. 2023 Mar 30;25:e46700. doi: 10.2196/46700.
Brauneck and colleagues have combined technical and legal perspectives in their timely and valuable paper "Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review." Researchers who design mobile health (mHealth) systems must adopt the same privacy-by-design approach that privacy regulations (eg, General Data Protection Regulation) do. In order to do this successfully, we will have to overcome implementation challenges in privacy-enhancing technologies such as differential privacy. We will also have to pay close attention to emerging technologies such as private synthetic data generation.
布劳内克（Brauneck）及其同事在他们及时且有价值的论文《医学研究中的联合机器学习、隐私增强技术和数据保护法：范围综述》中结合了技术和法律视角。设计移动医疗（mHealth）系统的研究人员必须采用隐私法规（例如，通用数据保护条例）所采用的同样隐私设计方法。为了成功做到这一点，我们将必须克服隐私增强技术（例如差分隐私）的实施挑战。我们还将必须密切关注私人合成数据生成等新兴技术。
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