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Privacy-preserving data sharing infrastructures for medical research: systematization and comparison

机译：保留隐私数据共享基础设施的医学研究：系统化与比较

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Data sharing is considered a crucial part of modern medical research. Unfortunately, despite its advantages, it often faces obstacles, especially data privacy challenges. As a result, various approaches and infrastructures have been developed that aim to ensure that patients and research participants remain anonymous when data is shared. However, privacy protection typically comes at a cost, e.g. restrictions regarding the types of analyses that can be performed on shared data. What is lacking is a systematization making the trade-offs taken by different approaches transparent. The aim of the work described in this paper was to develop a systematization for the degree of privacy protection provided and the trade-offs taken by different data sharing methods. Based on this contribution, we categorized popular data sharing approaches and identified research gaps by analyzing combinations of promising properties and features that are not yet supported by existing approaches. The systematization consists of different axes. Three axes relate to privacy protection aspects and were adopted from the popular Five Safes Framework: (1) safe data, addressing privacy at the input level, (2) safe settings, addressing privacy during shared processing, and (3) safe outputs, addressing privacy protection of analysis results. Three additional axes address the usefulness of approaches: (4) support for de-duplication, to enable the reconciliation of data belonging to the same individuals, (5) flexibility, to be able to adapt to different data analysis requirements, and (6) scalability, to maintain performance with increasing complexity of shared data or common analysis processes. Using the systematization, we identified three different categories of approaches: distributed data analyses, which exchange anonymous aggregated data, secure multi-party computation protocols, which exchange encrypted data, and data enclaves, which store pooled individual-level data in secure environments for access for analysis purposes. We identified important research gaps, including a lack of approaches enabling the de-duplication of horizontally distributed data or providing a high degree of flexibility. There are fundamental differences between different data sharing approaches and several gaps in their functionality that may be interesting to investigate in future work. Our systematization can make the properties of privacy-preserving data sharing infrastructures more transparent and support decision makers and regulatory authorities with a better understanding of the trade-offs taken.
机译：数据共享被认为是现代医学研究的关键部分。不幸的是，尽管有其优势，但它经常面临障碍，尤其是数据隐私挑战。因此，已经开发了各种方法和基础设施，旨在确保在共享数据时确保患者和研究参与者保持匿名。但是，隐私保护通常以成本为本，例如，关于可以在共享数据执行的分析类型的限制。缺乏的是系统化，使不同方法透明的权衡。本文描述的工作的目的是为所提供的隐私保护程度和不同数据共享方法采取的权衡制定了系统化。基于这一贡献，我们通过分析现有方法尚未支持的有希望的性能和特征的组合分类了流行的数据共享方法并确定了研究差距。系统化由不同的轴组成。三个轴涉及隐私保护方面，并由流行的五个保险柜框架采用：（1）安全数据，在输入级别的隐私，（2）安全设置，在共享处理期间寻址隐私，以及（3）安全输出，寻址隐私保护分析结果。三个额外的轴址解决方法的有用性：（4）支持重复，以使得属于同一个人的数据和解，（5）灵活性，能够适应不同的数据分析要求，（6）可扩展性，以增加共享数据或共同分析过程的复杂性维持性能。使用Systematization，我们确定了三种不同类别的方法：分布式数据分析，它交换匿名聚合数据，安全的多方计算协议，它在安全环境中存储汇集的单个数据，在安全环境中存储汇总的单个数据，该数据分析用于分析目的。我们确定了重要的研究差距，包括缺乏方法，使水平分布数据的重复或提供高度的灵活性。不同数据共享方法之间存在根本差异，以及在其功能中的几个差距可能很有趣，以便在未来的工作中调查。我们的系统化可以使隐私保留数据共享基础设施更加透明和支持决策者和监管机构的性质，并更好地了解所采取的权衡。

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