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生物医学数据科学中的隐私增强技术。

Privacy-Enhancing Technologies in Biomedical Data Science.

机构信息

Department of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut, USA; email:

Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA; email:

出版信息

Annu Rev Biomed Data Sci. 2024 Aug;7(1):317-343. doi: 10.1146/annurev-biodatasci-120423-120107.

Abstract

The rapidly growing scale and variety of biomedical data repositories raise important privacy concerns. Conventional frameworks for collecting and sharing human subject data offer limited privacy protection, often necessitating the creation of data silos. Privacy-enhancing technologies (PETs) promise to safeguard these data and broaden their usage by providing means to share and analyze sensitive data while protecting privacy. Here, we review prominent PETs and illustrate their role in advancing biomedicine. We describe key use cases of PETs and their latest technical advances and highlight recent applications of PETs in a range of biomedical domains. We conclude by discussing outstanding challenges and social considerations that need to be addressed to facilitate a broader adoption of PETs in biomedical data science.

摘要

生物医学数据存储库的规模和种类迅速增长,引发了重要的隐私问题。传统的收集和共享人类受试者数据的框架提供的隐私保护有限,通常需要创建数据孤岛。隐私增强技术(PET)有望通过提供共享和分析敏感数据的同时保护隐私的手段来保护这些数据并扩大其使用范围。在这里,我们回顾了突出的 PET,并说明了它们在推进生物医学中的作用。我们描述了 PET 的关键用例及其最新技术进展,并强调了 PET 在一系列生物医学领域中的最新应用。最后,我们讨论了需要解决的突出挑战和社会考虑因素,以促进 PET 在生物医学数据科学中的更广泛应用。

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