Statistical Data Integration for Health Policy Evidence-Building
Health policy evidence-building requires data sources such as health care claims, electronic
health records, probability and nonprobability survey data, epidemiological surveillance …
health records, probability and nonprobability survey data, epidemiological surveillance …
Evaluating bias and noise induced by the US Census Bureau's privacy protection methods
The US Census Bureau faces a difficult trade-off between the accuracy of Census statistics
and the protection of individual information. We conduct an independent evaluation of bias …
and the protection of individual information. We conduct an independent evaluation of bias …
Privacy-preserving and fairness-aware federated learning for critical infrastructure protection and resilience
The energy industry is undergoing significant transformations as it strives to achieve net-
zero emissions and future-proof its infrastructure, where every participant in the power grid …
zero emissions and future-proof its infrastructure, where every participant in the power grid …
When Privacy Protection Goes Wrong: How and Why the 2020 Census Confidentiality Program Failed
S Ruggles - Journal of Economic Perspectives, 2024 - pubs.aeaweb.org
Abstract The US Census Bureau implemented a new disclosure control strategy for the 2020
Census that adds deliberate error to every population statistic for every geographic unit …
Census that adds deliberate error to every population statistic for every geographic unit …
A data-driven approach to choosing privacy parameters for clinical trial data sharing under differential privacy
Objectives Clinical trial data sharing is crucial for promoting transparency and collaborative
efforts in medical research. Differential privacy (DP) is a formal statistical technique for …
efforts in medical research. Differential privacy (DP) is a formal statistical technique for …
Towards more accurate and useful data anonymity vulnerability measures
P Francis, D Wagner - arXiv preprint arXiv:2403.06595, 2024 - arxiv.org
The purpose of anonymizing structured data is to protect the privacy of individuals in the
data while retaining the statistical properties of the data. There is a large body of work that …
data while retaining the statistical properties of the data. There is a large body of work that …
An examination of the alleged privacy threats of confidence-ranked reconstruction of Census microdata
The threat of reconstruction attacks has led the US Census Bureau (USCB) to replace in the
Decennial Census 2020 the traditional statistical disclosure limitation based on rank …
Decennial Census 2020 the traditional statistical disclosure limitation based on rank …
Equitable differential privacy
V Kaul, T Mukherjee - Frontiers in Big Data, 2024 - frontiersin.org
Differential privacy (DP) has been in the public spotlight since the announcement of its use
in the 2020 US Census. While DP algorithms have substantially improved the confidentiality …
in the 2020 US Census. While DP algorithms have substantially improved the confidentiality …
Synthetic Census Data Generation via Multidimensional Multiset Sum
The US Decennial Census provides valuable data for both research and policy purposes.
Census data are subject to a variety of disclosure avoidance techniques prior to release in …
Census data are subject to a variety of disclosure avoidance techniques prior to release in …
Differential Privacy Protections in 2020 US Decennial Census Data Do Not Impede Measurement of Racial and Ethnic Disparities
J Snoke, A Haas, SC Martino… - Medical Care Research …, 2024 - journals.sagepub.com
Census data are vital to health care research but must also protect respondents'
confidentiality. The 2020 decennial Census employs a new Differential Privacy framework; …
confidentiality. The 2020 decennial Census employs a new Differential Privacy framework; …