{"title":"为实现敏感数据研究的民主化,我们应该让合成数据更容易获取","authors":"Erik-Jan van Kesteren","doi":"arxiv-2404.17271","DOIUrl":null,"url":null,"abstract":"For over 30 years, synthetic data has been heralded as a promising solution\nto make sensitive datasets accessible. However, despite much research effort\nand several high-profile use-cases, the widespread adoption of synthetic data\nas a tool for open, accessible, reproducible research with sensitive data is\nstill a distant dream. In this opinion, Erik-Jan van Kesteren, head of the\nODISSEI Social Data Science team, argues that in order to progress towards\nwidespread adoption of synthetic data as a privacy enhancing technology, the\ndata science research community should shift focus away from developing better\nsynthesis methods: instead, it should develop accessible tools, educate peers,\nand publish small-scale case studies.","PeriodicalId":501323,"journal":{"name":"arXiv - STAT - Other Statistics","volume":"7 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"To democratize research with sensitive data, we should make synthetic data more accessible\",\"authors\":\"Erik-Jan van Kesteren\",\"doi\":\"arxiv-2404.17271\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For over 30 years, synthetic data has been heralded as a promising solution\\nto make sensitive datasets accessible. However, despite much research effort\\nand several high-profile use-cases, the widespread adoption of synthetic data\\nas a tool for open, accessible, reproducible research with sensitive data is\\nstill a distant dream. In this opinion, Erik-Jan van Kesteren, head of the\\nODISSEI Social Data Science team, argues that in order to progress towards\\nwidespread adoption of synthetic data as a privacy enhancing technology, the\\ndata science research community should shift focus away from developing better\\nsynthesis methods: instead, it should develop accessible tools, educate peers,\\nand publish small-scale case studies.\",\"PeriodicalId\":501323,\"journal\":{\"name\":\"arXiv - STAT - Other Statistics\",\"volume\":\"7 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - STAT - Other Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2404.17271\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - STAT - Other Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2404.17271","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
30 多年来,合成数据一直被认为是使敏感数据集可访问的一种有前途的解决方案。然而,尽管做了大量的研究工作,也有几个备受瞩目的使用案例,但要广泛采用合成数据作为开放、可访问、可重复的敏感数据研究工具,仍然是一个遥远的梦想。在本文中,ODISSEI 社会数据科学团队负责人 Erik-Jan van Kesteren 认为,为了将合成数据作为隐私增强技术广泛采用,数据科学研究界应将重点从开发更好的合成方法转移到开发可访问的工具、教育同行和发布小规模案例研究上来。
To democratize research with sensitive data, we should make synthetic data more accessible
For over 30 years, synthetic data has been heralded as a promising solution
to make sensitive datasets accessible. However, despite much research effort
and several high-profile use-cases, the widespread adoption of synthetic data
as a tool for open, accessible, reproducible research with sensitive data is
still a distant dream. In this opinion, Erik-Jan van Kesteren, head of the
ODISSEI Social Data Science team, argues that in order to progress towards
widespread adoption of synthetic data as a privacy enhancing technology, the
data science research community should shift focus away from developing better
synthesis methods: instead, it should develop accessible tools, educate peers,
and publish small-scale case studies.