Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Claire McKay Bowen"'
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 14, Iss 3 (2024)
The authors discuss their experience applying differential privacy with a complex data set with the goal of enabling standard approaches to statistical data analysis. They highlight lessons learned and roadblocks encountered, distilling them into inc
Externí odkaz:
https://doaj.org/article/e7cdd2364e394dc8a70572941683514e
Autor:
Claire McKay Bowen
Publikováno v:
Statistics and Public Policy, Vol 10, Iss 1 (2023)
AbstractWhile the threat of biased AI has received considerable attention, another invisible threat to data democracy exists that has not received scientific or media attention. This threat is the lack of autonomy for the 13 principal United States f
Externí odkaz:
https://doaj.org/article/444149b4eeaf44b59b29d7366d010e04
Autor:
Claire McKay Bowen, Joshua Snoke
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 11, Iss 1 (2021)
Differentially private synthetic data generation offers a recent solution to release analytically useful data while preserving the privacy of individuals in the data. In order to utilize these algorithms for public policy decisions, policymakers need
Externí odkaz:
https://doaj.org/article/4365591cccbf4b19ad5546081734b762
Publikováno v:
Journal of Data Science. :173-176
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7ea8a62164cefa4485c0a29cf1db2453
https://doi.org/10.6339/23-jds212edi
https://doi.org/10.6339/23-jds212edi
Autor:
Claire McKay Bowen, Victoria L. Bryant, Leonard Burman, Surachai Khitatrakun, Robert McClelland, Livia Mucciolo, Madeline Pickens, Aaron R. Williams
Publikováno v:
National Tax Journal. 75:767-790
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d2bffdbf156c017b281217f9926e23dc
https://doi.org/10.1086/722094
https://doi.org/10.1086/722094
Autor:
Michael Messner, Claire McKay Bowen
Publikováno v:
CHANCE. 35:4-8
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ec520de084bc33e2f84cb09ccb8a581e
https://doi.org/10.1080/09332480.2022.2123147
https://doi.org/10.1080/09332480.2022.2123147
Autor:
Aaron R. Williams, Claire McKay Bowen
Publikováno v:
WIREs Computational Statistics.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b7418f352eede590f3fdba8b126fdd71
https://doi.org/10.1002/wics.1615
https://doi.org/10.1002/wics.1615
Publikováno v:
METRON. 79:1-31
Differential Privacy (DP) formalizes privacy in mathematical terms and provides a robust concept for privacy protection. DIfferentially Private Data Synthesis (DIPS) techniques produce and release synthetic individual-level data in the DP framework.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d81c0b8662939beb369433f758234619
https://doi.org/10.1007/s40300-021-00201-0
https://doi.org/10.1007/s40300-021-00201-0
Autor:
Joshua Snoke, Claire McKay Bowen
Publikováno v:
CHANCE. 33:6-13
Suppose you had a data set that contained records of individuals, including demographics such as their age, sex, and race. Suppose also that these data contained additional in-depth personal inform...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::68b52ecc4e3a3c012dcf36d781024315
https://doi.org/10.1080/09332480.2020.1847947
https://doi.org/10.1080/09332480.2020.1847947
Autor:
Claire McKay Bowen, Victoria Bryant, Leonard Burman, John Czajka, Surachai Khitatrakun, Graham MacDonald, Robert McClelland, Livia Mucciolo, Madeline Pickens, Kyle Ueyama, Aaron R. Williams, Doug Wissoker, Noah Zwiefel
Publikováno v:
Privacy in Statistical Databases ISBN: 9783031139444
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cdfd02a36c7bff07e2671a3e33be7c75
https://doi.org/10.1007/978-3-031-13945-1_14
https://doi.org/10.1007/978-3-031-13945-1_14
