Pub Date : 2022-07-28DOI: 10.1162/99608f92.b9e4ceec
L. Tabak, Lyric A. Jorgenson, Maryann E. Martone, Richard K. Nakamura
{"title":"Conversation with Dr. Lawrence Tabak and Dr. Lyric Jorgenson on the NIH Perspective on Data Sharing and Management","authors":"L. Tabak, Lyric A. Jorgenson, Maryann E. Martone, Richard K. Nakamura","doi":"10.1162/99608f92.b9e4ceec","DOIUrl":"https://doi.org/10.1162/99608f92.b9e4ceec","url":null,"abstract":"","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48092123","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}
Pub Date : 2022-07-28DOI: 10.1162/99608f92.5ff070bf
Kristen B. Rosati
{"title":"Legal Compliance and Good Data Stewardship in Data Sharing Plans","authors":"Kristen B. Rosati","doi":"10.1162/99608f92.5ff070bf","DOIUrl":"https://doi.org/10.1162/99608f92.5ff070bf","url":null,"abstract":"","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47006461","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}
Pub Date : 2022-07-28DOI: 10.1162/99608f92.42285dcb
D. Parkes
{"title":"Building a More Robust Data Science, Toward a More Robust World","authors":"D. Parkes","doi":"10.1162/99608f92.42285dcb","DOIUrl":"https://doi.org/10.1162/99608f92.42285dcb","url":null,"abstract":"","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46916903","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}
Pub Date : 2022-06-24DOI: 10.1162/99608f92.5cd8024e
Brian J. Asquith, Brad J. Hershbein, T. Kugler, S. Reed, S. Ruggles, Jonathan P. Schroeder, Steve Yesiltepe, David C. Van Riper
{"title":"Assessing the Impact of Differential Privacy on Measures of Population and Racial Residential Segregation","authors":"Brian J. Asquith, Brad J. Hershbein, T. Kugler, S. Reed, S. Ruggles, Jonathan P. Schroeder, Steve Yesiltepe, David C. Van Riper","doi":"10.1162/99608f92.5cd8024e","DOIUrl":"https://doi.org/10.1162/99608f92.5cd8024e","url":null,"abstract":"","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46722195","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}
Pub Date : 2022-06-24DOI: 10.1162/99608f92.1cfad278
J. Eltinge
{"title":"Disclosure Protection in the Context of Statistical Agency Operations: Data Quality and Related Constraints","authors":"J. Eltinge","doi":"10.1162/99608f92.1cfad278","DOIUrl":"https://doi.org/10.1162/99608f92.1cfad278","url":null,"abstract":"","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64442717","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}
Pub Date : 2022-06-24DOI: 10.1162/99608f92.5d9b1a8d
Ori Heffetz
{"title":"What Will It Take to Get to Acceptable Privacy-Accuracy Combinations?","authors":"Ori Heffetz","doi":"10.1162/99608f92.5d9b1a8d","DOIUrl":"https://doi.org/10.1162/99608f92.5d9b1a8d","url":null,"abstract":"","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45775975","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}
Pub Date : 2022-06-24DOI: 10.1162/99608f92.cb06b469
Ruobin Gong, E. Groshen, S. Vadhan
and relative accuracy population counts in total and by race for multiple geographic levels and compare commonly used measures of residential segregation. how the accuracy varies by the global privacy loss budget and by the allocation of the privacy loss budget to geographic levels and queries. The also that can indicate either notably or notably segregation in
{"title":"Harnessing the Known Unknowns: Differential Privacy and the 2020 Census","authors":"Ruobin Gong, E. Groshen, S. Vadhan","doi":"10.1162/99608f92.cb06b469","DOIUrl":"https://doi.org/10.1162/99608f92.cb06b469","url":null,"abstract":"and relative accuracy population counts in total and by race for multiple geographic levels and compare commonly used measures of residential segregation. how the accuracy varies by the global privacy loss budget and by the allocation of the privacy loss budget to geographic levels and queries. The also that can indicate either notably or notably segregation in","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46758324","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}
Pub Date : 2022-06-24DOI: 10.1162/99608f92.ff891fe5
V. Hotz, Joseph Salvo
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.
{"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":null,"pages":null},"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}
Pub Date : 2022-06-24DOI: 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":null,"pages":null},"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}
Pub Date : 2022-06-24DOI: 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":null,"pages":null},"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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：