To Link or Synthesize? An Approach to Data Quality Comparison

IF 1.5 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Duncan Smith, M. Elliot, J. Sakshaug
{"title":"To Link or Synthesize? An Approach to Data Quality Comparison","authors":"Duncan Smith, M. Elliot, J. Sakshaug","doi":"10.1145/3580487","DOIUrl":null,"url":null,"abstract":"Linking administrative data to produce more informative data for subsequent analysis has become an increasingly common practice. However, there might be concomitant risks of disclosing sensitive information about individuals. One practice that reduces these risks is data synthesis. In data synthesis the data are used to fit a model from which synthetic data are then generated. The synthetic data are then released to end users. There are some scenarios where an end user might have the option of using linked data or accepting synthesized data. However, linkage and synthesis are susceptible to errors that could limit their usefulness. Here, we investigate the problem of comparing the quality of linked data to synthesized data and demonstrate through simulations how the problem might be approached. These comparisons are important when considering how an end user can be supplied with the highest-quality data and in situations where one must consider risk/utility tradeoffs.","PeriodicalId":44355,"journal":{"name":"ACM Journal of Data and Information Quality","volume":"58 1","pages":"1 - 20"},"PeriodicalIF":1.5000,"publicationDate":"2023-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Journal of Data and Information Quality","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3580487","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 1

Abstract

Linking administrative data to produce more informative data for subsequent analysis has become an increasingly common practice. However, there might be concomitant risks of disclosing sensitive information about individuals. One practice that reduces these risks is data synthesis. In data synthesis the data are used to fit a model from which synthetic data are then generated. The synthetic data are then released to end users. There are some scenarios where an end user might have the option of using linked data or accepting synthesized data. However, linkage and synthesis are susceptible to errors that could limit their usefulness. Here, we investigate the problem of comparing the quality of linked data to synthesized data and demonstrate through simulations how the problem might be approached. These comparisons are important when considering how an end user can be supplied with the highest-quality data and in situations where one must consider risk/utility tradeoffs.
链接还是合成?一种数据质量比较方法
将管理数据联系起来以产生更多信息丰富的数据以供后续分析,已成为越来越普遍的做法。然而,泄露个人敏感信息可能会带来风险。减少这些风险的一种做法是数据综合。在数据合成中,数据用于拟合模型,然后从中生成合成数据。然后将合成数据发布给最终用户。在某些场景中,最终用户可以选择使用链接数据或接受合成数据。但是,连接和综合容易受到错误的影响,从而限制了它们的用途。在这里,我们研究了将链接数据的质量与合成数据进行比较的问题,并通过模拟演示了如何处理这个问题。在考虑如何向最终用户提供最高质量的数据以及必须考虑风险/效用权衡的情况下,这些比较非常重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
ACM Journal of Data and Information Quality
ACM Journal of Data and Information Quality COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
4.10
自引率
4.80%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信