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

{"title":"利用已知未知：差异隐私与2020年人口普查","authors":"Ruobin Gong, E. Groshen, S. Vadhan","doi":"10.1162/99608f92.cb06b469","DOIUrl":null,"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":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Harnessing the Known Unknowns: Differential Privacy and the 2020 Census\",\"authors\":\"Ruobin Gong, E. Groshen, S. Vadhan\",\"doi\":\"10.1162/99608f92.cb06b469\",\"DOIUrl\":null,\"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\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Harvard data science review\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1162/99608f92.cb06b469\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Harvard data science review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1162/99608f92.cb06b469","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 6

摘要

以及多个地理级别的总人口和按种族统计的相对准确性，并比较常用的居住隔离措施。准确性如何因全球隐私损失预算以及隐私损失预算对地理级别和查询的分配而变化。也可以表明在

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

Harnessing the Known Unknowns: Differential Privacy and the 2020 Census

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

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Harvard data science review

自引率

0.00%

发文量