Empowering open data sharing for social good: a privacy-aware approach.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-02-12 DOI:10.1038/s41597-025-04506-x
Tânia Carvalho, Luís Antunes, Cristina Costa Santos, Nuno Moniz
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Abstract

The Covid-19 pandemic has affected the world at multiple levels. Data sharing was pivotal for advancing research to understand the underlying causes and implement effective containment strategies. In response, many countries have facilitated access to daily cases to support research initiatives, fostering collaboration between organisations and making such data available to the public through open data platforms. Despite the several advantages of data sharing, one of the major concerns before releasing health data is its impact on individuals' privacy. Such a sharing process should adhere to state-of-the-art methods in Data Protection by Design and by Default. In this paper, we use a Covid-19 data set from Portugal's second-largest hospital to show how it is feasible to ensure data privacy while improving the quality and maintaining the utility of the data. Our goal is to demonstrate how knowledge exchange in multidisciplinary teams of healthcare practitioners, data privacy, and data science experts is crucial to co-developing strategies that ensure high utility in de-identified data.

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Covid-19 大流行在多个层面对世界造成了影响。数据共享对于推动研究以了解根本原因和实施有效的遏制战略至关重要。为此,许多国家为获取日常病例提供了便利,以支持研究计划,促进组织间的合作,并通过开放数据平台向公众提供此类数据。尽管数据共享具有多种优势,但在发布健康数据之前,人们主要关注的问题之一是其对个人隐私的影响。这种共享过程应遵循数据保护设计和默认的最先进方法。在本文中,我们使用葡萄牙第二大医院的 Covid-19 数据集来展示如何在确保数据隐私的同时提高数据质量并保持数据的实用性。我们的目标是展示由医疗从业人员、数据隐私和数据科学专家组成的多学科团队中的知识交流对于共同制定确保去标识化数据高度实用性的策略是如何至关重要的。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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