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Harnessing the Known Unknowns: Differential Privacy and the 2020 Census 利用已知未知：差异隐私与2020年人口普查
Harvard data science review Pub Date : 2022-06-24 DOI: 10.1162/99608f92.cb06b469
Ruobin Gong, E. Groshen, S. Vadhan
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引用次数: 6
A Chronicle of the Application of Differential Privacy to the 2020 Census 差别隐私在2020年人口普查中的应用纪事
Harvard data science review Pub Date : 2022-06-24 DOI: 10.1162/99608f92.ff891fe5
V. Hotz, Joseph Salvo
{"title":"A Chronicle of the Application of Differential Privacy to the 2020 Census","authors":"V. Hotz, Joseph Salvo","doi":"10.1162/99608f92.ff891fe5","DOIUrl":"https://doi.org/10.1162/99608f92.ff891fe5","url":null,"abstract":"In this article, we chronicle the U.S. Census Bureau’s development of the Disclosure Avoidance System (DAS) for the publicly released products of the 2020 Census of Population. We provide a brief history of the Census Bureau’s fulfillment of its dual mission of conducting and disseminating the constitutionally mandated decennial information on the U.S. population and its promise of safeguarding the confidentiality of that information. We discuss the basis for and development of a new DAS for released data products from the 2020 Census and the evidence that emerged from various user communities on the accuracy and usability of data produced under this new DAS. We offer some assessments of this experience, the dilemmas and challenges that the Census Bureau faces for producing usable data while safeguarding the confidentiality of the information it collects, and some recommendations for addressing these challenges in the future.","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42721584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 9
Private Numbers in Public Policy: Census, Differential Privacy, and Redistricting 公共政策中的私人数字：人口普查、差别隐私和重新划分选区
Harvard data science review Pub Date : 2022-06-24 DOI: 10.1162/99608f92.22fd8a0e
A. Cohen, M. Duchin, J. Matthews, Bhushan Suwal
{"title":"Private Numbers in Public Policy: Census, Differential Privacy, and Redistricting","authors":"A. Cohen, M. Duchin, J. Matthews, Bhushan Suwal","doi":"10.1162/99608f92.22fd8a0e","DOIUrl":"https://doi.org/10.1162/99608f92.22fd8a0e","url":null,"abstract":"","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45490087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
The Effect of Differentially Private Noise Injection on Sampling Efficiency and Funding Allocations: Evidence From the 1940 Census 差别私人噪声注入对采样效率和资金分配的影响：来自1940年人口普查的证据
Harvard data science review Pub Date : 2022-06-24 DOI: 10.1162/99608f92.a93d96fa
Quentin Brummet, E. Mulrow, K. Wolter
{"title":"The Effect of Differentially Private Noise Injection on Sampling Efficiency and Funding Allocations: Evidence From the 1940 Census","authors":"Quentin Brummet, E. Mulrow, K. Wolter","doi":"10.1162/99608f92.a93d96fa","DOIUrl":"https://doi.org/10.1162/99608f92.a93d96fa","url":null,"abstract":"","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42201836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Disclosure Avoidance and the 2020 Census: What Do Researchers Need to Know? 避免披露和2020年人口普查:研究人员需要知道什么?
Harvard data science review Pub Date : 2022-06-24 DOI: 10.1162/99608f92.aed7f34f
E. Groshen, D. Goroff
{"title":"Disclosure Avoidance and the 2020 Census: What Do Researchers Need to Know?","authors":"E. Groshen, D. Goroff","doi":"10.1162/99608f92.aed7f34f","DOIUrl":"https://doi.org/10.1162/99608f92.aed7f34f","url":null,"abstract":"","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46122023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 20
Differential Perspectives: Epistemic Disconnects Surrounding the U.S. Census Bureau’s Use of Differential Privacy 差异视角:围绕美国人口普查局使用差异隐私的认知脱节
Harvard data science review Pub Date : 2022-06-24 DOI: 10.1162/99608f92.66882f0e
Dan R. Boyd, Jayshree Sarathy
{"title":"Differential Perspectives: Epistemic Disconnects Surrounding the U.S. Census Bureau’s Use of Differential Privacy","authors":"Dan R. Boyd, Jayshree Sarathy","doi":"10.1162/99608f92.66882f0e","DOIUrl":"https://doi.org/10.1162/99608f92.66882f0e","url":null,"abstract":": When the U.S. Census Bureau announced its intention to modernize its disclosure avoidance procedures for the 2020 Census, it sparked a controversy that is still underway. The move to differential privacy introduced technical and procedural uncertainties, leaving stakeholders unable to evaluate the quality of the data. More importantly, this transformation exposed the statistical illusions and limitations of census data, weakening stakeholders’ trust in the data and in the Census Bureau itself. This essay examines the epistemic currents of this controversy. Drawing on theories from Science and Technology Studies (STS) and ethnographic fieldwork, we analyze the current controversy over differential privacy as a battle over uncertainty, trust, and legitimacy of the Census. We argue that rebuilding trust will require more than technical repairs or improved communication; it will require reconstructing what we identify as a ‘statistical imaginary.’","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47874950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 20
How Can We Make Data Science Even More Valuable? 我们如何让数据科学更有价值？
Harvard data science review Pub Date : 2022-04-28 DOI: 10.1162/99608f92.13e4351b
F. Dominici
{"title":"How Can We Make Data Science Even More Valuable?","authors":"F. Dominici","doi":"10.1162/99608f92.13e4351b","DOIUrl":"https://doi.org/10.1162/99608f92.13e4351b","url":null,"abstract":"","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47677996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Unlocking Government Data to Support and Evaluate Scientific Research: An Overview of Selected Papers from the Value of Science Conference 解锁政府数据以支持和评估科学研究:科学价值会议精选论文综述
Harvard data science review Pub Date : 2022-04-28 DOI: 10.1162/99608f92.373a3313
Henry A. Kautz
{"title":"Unlocking Government Data to Support and Evaluate Scientific Research: An Overview of Selected Papers from the Value of Science Conference","authors":"Henry A. Kautz","doi":"10.1162/99608f92.373a3313","DOIUrl":"https://doi.org/10.1162/99608f92.373a3313","url":null,"abstract":"","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42195135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Data Fragmentation and Data Linking: A Threat and an Opportunity 数据碎片化和数据链接：威胁与机遇
Harvard data science review Pub Date : 2022-04-28 DOI: 10.1162/99608f92.946ef791
Clemencia Cosentino
{"title":"Data Fragmentation and Data Linking: A Threat and an Opportunity","authors":"Clemencia Cosentino","doi":"10.1162/99608f92.946ef791","DOIUrl":"https://doi.org/10.1162/99608f92.946ef791","url":null,"abstract":"","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45268907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
The Value of Science: Special Theme 科学的价值:专题
Harvard data science review Pub Date : 2022-04-28 DOI: 10.1162/99608f92.4a01213d
Julia I. Lane, Brian Kim, F. Kreuter, Allison Nunez
{"title":"The Value of Science: Special Theme","authors":"Julia I. Lane, Brian Kim, F. Kreuter, Allison Nunez","doi":"10.1162/99608f92.4a01213d","DOIUrl":"https://doi.org/10.1162/99608f92.4a01213d","url":null,"abstract":"","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64442764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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