{"title":"Empowering Open Data Sharing for Social Good: A Privacy-Aware Approach","authors":"Tânia Carvalho, Luís Antunes, Cristina Costa, Nuno Moniz","doi":"arxiv-2408.17378","DOIUrl":null,"url":null,"abstract":"The Covid-19 pandemic has affected the world at multiple levels. Data sharing\nwas pivotal for advancing research to understand the underlying causes and\nimplement effective containment strategies. In response, many countries have\npromoted the availability of daily cases to support research initiatives,\nfostering collaboration between organisations and making such data available to\nthe public through open data platforms. Despite the several advantages of data\nsharing, one of the major concerns before releasing health data is its impact\non individuals' privacy. Such a sharing process should be based on\nstate-of-the-art methods in Data Protection by Design and by Default. In this\npaper, we use a data set related to Covid-19 cases in the second largest\nhospital in Portugal to show how it is feasible to ensure data privacy while\nimproving the quality and maintaining the utility of the data. Our goal is to\ndemonstrate how knowledge exchange in multidisciplinary teams of healthcare\npractitioners, data privacy, and data science experts is crucial to\nco-developing strategies that ensure high utility of de-identified data.","PeriodicalId":501123,"journal":{"name":"arXiv - CS - Databases","volume":"48 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Databases","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.17378","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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
promoted the availability of 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 be based on
state-of-the-art methods in Data Protection by Design and by Default. In this
paper, we use a data set related to Covid-19 cases in the second largest
hospital in Portugal 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 of de-identified data.