{"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}
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