Hyunghoon Cho, David Froelicher, Natnatee Dokmai, Anupama Nandi, Shuvom Sadhuka, Matthew M Hong, Bonnie Berger
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引用次数: 0
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.
生物医学数据储存库的规模和种类迅速增加，引起了人们对隐私问题的关注。收集和共享人体数据的传统框架对隐私的保护有限，往往需要建立数据孤岛。隐私增强技术（PET）有望在保护隐私的同时，通过提供共享和分析敏感数据的方法来保护这些数据并扩大其使用范围。在此，我们回顾了著名的 PET，并说明了它们在推动生物医学发展方面的作用。我们描述了 PET 的关键用例及其最新技术进展，并重点介绍了 PET 在一系列生物医学领域的最新应用。最后，我们讨论了在生物医学数据科学中更广泛地采用 PETs 所面临的挑战和需要解决的社会问题。
期刊介绍：
The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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