{"title":"To democratize research with sensitive data, we should make synthetic data more accessible","authors":"Erik-Jan van Kesteren","doi":"10.1016/j.patter.2024.101049","DOIUrl":null,"url":null,"abstract":"<p>For over 30 years, synthetic data have been heralded as a solution to make sensitive datasets accessible. However, despite much research effort, its adoption as a tool for research with sensitive data is lacking. This article argues that to make progress in this regard, the data science community should focus on improving the accessibility of existing privacy-friendly synthesis techniques.</p>","PeriodicalId":36242,"journal":{"name":"Patterns","volume":null,"pages":null},"PeriodicalIF":6.7000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Patterns","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.patter.2024.101049","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
For over 30 years, synthetic data have been heralded as a solution to make sensitive datasets accessible. However, despite much research effort, its adoption as a tool for research with sensitive data is lacking. This article argues that to make progress in this regard, the data science community should focus on improving the accessibility of existing privacy-friendly synthesis techniques.