Claire McKay Bowen
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View article: But Can You Use It? Design Recommendations for Practical Alternatives to Differentially Private Interactive Systems
But Can You Use It? Design Recommendations for Practical Alternatives to Differentially Private Interactive Systems Open
View article: But Can You Use It? Design Recommendations for Differentially Private Interactive Systems
But Can You Use It? Design Recommendations for Differentially Private Interactive Systems Open
Accessing data collected by federal statistical agencies is essential for public policy research and improving evidence-based decision making, such as evaluating the effectiveness of social programs, understanding demographic shifts, or ad…
View article: The Nation’s Data at Risk: The First Annual Report on the Federal Statistical System
The Nation’s Data at Risk: The First Annual Report on the Federal Statistical System Open
View article: Auerbach et al.'s contribution to the Discussion of ‘Independent review of the UK Statistics Authority' by Lievesley
Auerbach et al.'s contribution to the Discussion of ‘Independent review of the UK Statistics Authority' by Lievesley Open
View article: Incompatibilities Between Current Practices in Statistical Data Analysis and Differential Privacy
Incompatibilities Between Current Practices in Statistical Data Analysis and Differential Privacy Open
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 th…
View article: Disclosing Economists’ Privacy Perspectives: A Survey of American Economic Association Members’ Views on Differential Privacy and the Usability of Noise-Infused Data
Disclosing Economists’ Privacy Perspectives: A Survey of American Economic Association Members’ Views on Differential Privacy and the Usability of Noise-Infused Data Open
Policymakers often rely on official statistics and administrative data to make essential public policy decisions, such as using administrative tax data to broaden our understanding of individuals' and firms' responses to economic incentive…
View article: Government Data of the People, by the People, for the People: Navigating Citizen Privacy Concerns
Government Data of the People, by the People, for the People: Navigating Citizen Privacy Concerns Open
The data privacy community generally agrees that government data should be more widely accessible, especially being of the people (data collected about them), by the people (collected and supported using taxpayer dollars), and for the peop…
View article: The Case for Researching Applied Privacy Enhancing Technologies
The Case for Researching Applied Privacy Enhancing Technologies Open
View article: Statistical Analysis to Support Improved Student Outcomes
Statistical Analysis to Support Improved Student Outcomes Open
A supply of individuals trained in STEM is needed to meet the employment needs of the United States. To address this need, an analytics initiative was executed to analyze multiple data streams relevant to education and learning. The goal o…
View article: Incompatibilities Between Current Practices in Statistical Data Analysis and Differential Privacy
Incompatibilities Between Current Practices in Statistical Data Analysis and Differential Privacy Open
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 th…
View article: Advancing Microdata Privacy Protection: A Review of Synthetic Data
Advancing Microdata Privacy Protection: A Review of Synthetic Data Open
Synthetic data generation is a powerful tool for privacy protection when considering public release of record-level data files. Initially proposed about three decades ago, it has generated significant research and application interest. To …
View article: Editorial: Symposium Data Science and Statistics 2022
Editorial: Symposium Data Science and Statistics 2022 Open
Publisher: School of Statistics, Renmin University of China, Journal: Journal of Data Science, Title: Editorial - Symposium on Data Science and Statistics 2022, Authors: Claire McKay Bowen, Michael J. Grosskopf
View article: A Feasibility Study of Differentially Private Summary Statistics and Regression Analyses with Evaluations on Administrative and Survey Data
A Feasibility Study of Differentially Private Summary Statistics and Regression Analyses with Evaluations on Administrative and Survey Data Open
Federal administrative data, such as tax data, are invaluable for research, but because of privacy concerns, access to these data is typically limited to select agencies and a few individuals. An alternative to sharing microlevel data is t…
View article: Data Privacy via Integration of Differential Privacy and Data Synthesis
Data Privacy via Integration of Differential Privacy and Data Synthesis Open
When sharing data among collaborators or releasing data publicly, one of the crucial concerns is the extreme risk of exposing personal information of individuals who contribute to the data. Many statistical methods of data privacy and conf…
View article: Expectations for the 2020 Decennial Census and How They Stood Up to Scrutiny.
Expectations for the 2020 Decennial Census and How They Stood Up to Scrutiny. Open
The importance of the decennial census is clear, but the 2020 Census faced unprecedented challenges with unknown effects on data quality. To assist data users in identifying deviations between expected counts and the released counts across…
View article: Differentially Private Synthesis and Sharing of Network Data via Bayesian Exponential Random Graph Models.
Differentially Private Synthesis and Sharing of Network Data via Bayesian Exponential Random Graph Models. Open
information significantly better than two other approaches in network structural statistics and inference for ERGMs and latent space models. Furthermore, DP-ERGM satisfies node DP through modeling the global network structure with ERGM, a …
View article: The Art of Data Privacy
The Art of Data Privacy Open
Statistics are vital for understanding society, but they can pose a risk to the privacy of individuals who contribute their data. Claire McKay Bowen illustrates some of the methods used to minimise that risk – with the aid of a famous artw…
View article: Differentially Private Synthesis and Sharing of Network Data via Bayesian Exponential Random Graph Models.
Differentially Private Synthesis and Sharing of Network Data via Bayesian Exponential Random Graph Models. Open
information significantly better than two other approaches in network structural statistics and inference for ERGMs and latent space models. Furthermore, DP-ERGM satisfies node DP through modeling the global network structure with ERGM, a …
View article: A Feasibility Study of Differentially Private Summary Statistics and Regression Analyses with Evaluations on Administrative and Survey Data
A Feasibility Study of Differentially Private Summary Statistics and Regression Analyses with Evaluations on Administrative and Survey Data Open
Federal administrative data, such as tax data, are invaluable for research, but because of privacy concerns, access to these data is typically limited to select agencies and a few individuals. An alternative to sharing microlevel data is t…
View article: A Feasibility Study of Differentially Private Summary Statistics and Regression Analyses for Administrative Tax Data.
A Feasibility Study of Differentially Private Summary Statistics and Regression Analyses for Administrative Tax Data. Open
Federal administrative tax data are invaluable for research, but because of privacy concerns, access to these data is typically limited to select agencies and a few individuals. An alternative to sharing microlevel data are validation serv…
View article: The Philosophy of Differential Privacy
The Philosophy of Differential Privacy Open
Differential privacy is under attack.In 2006, a group of computer scientists invented differential privacy as an approach to provide mathematically provable privacy guarantees for statistical data
View article: Comparative Study of Differentially Private Synthetic Data Algorithms from the NIST PSCR Differential Privacy Synthetic Data Challenge
Comparative Study of Differentially Private Synthetic Data Algorithms from the NIST PSCR Differential Privacy Synthetic Data Challenge Open
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, policy…
View article: Differentially Private Generation of Social Networks via Exponential Random Graph Models
Differentially Private Generation of Social Networks via Exponential Random Graph Models Open
Many social networks contain sensitive relational information. One approach to protect the sensitive relational information while offering flexibility for social network research and analysis is to release synthetic social networks at a pr…
View article: Comparative Study of Differentially Private Data Synthesis Methods
Comparative Study of Differentially Private Data Synthesis Methods Open
When sharing data among researchers or releasing data for public use, there is a risk of exposing sensitive information of individuals in the data set. Data synthesis is a statistical disclosure limitation technique for releasing synthetic…
View article: Comparative Study of Differentially Private Synthetic Data Algorithms\n from the NIST PSCR Differential Privacy Synthetic Data Challenge
Comparative Study of Differentially Private Synthetic Data Algorithms\n from the NIST PSCR Differential Privacy Synthetic Data Challenge Open
Differentially private synthetic data generation offers a recent solution to\nrelease analytically useful data while preserving the privacy of individuals in\nthe data. In order to utilize these algorithms for public policy decisions,\npol…
View article: Comparative Study of Differentially Private Synthetic Data Algorithms and Evaluation Standards.
Comparative Study of Differentially Private Synthetic Data Algorithms and Evaluation Standards. Open
View article: Differentially Private Data Release via Statistical Election to Partition Sequentially
Differentially Private Data Release via Statistical Election to Partition Sequentially Open
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 …
View article: STatistical Election to Partition Sequentially (STEPS) and Its Application in Differentially Private Release and Analysis of Youth Voter Registration Data
STatistical Election to Partition Sequentially (STEPS) and Its Application in Differentially Private Release and Analysis of Youth Voter Registration Data Open
Voter data is important in political science research and applications such as improving youth voter turnout. Privacy protection is imperative in voter data since it often contains sensitive individual information. Differential privacy (DP…
View article: Differentially Private Data Synthesis Methods
Differentially Private Data Synthesis Methods Open
When sharing data among researchers or releasing data for public use, there is a risk of exposing sensitive information of individuals who contribute to the data. Data synthesis (DS) is a statistical disclosure limitation technique for rel…