54 citations · 59 across the 8 of their papers we have counts for
8 papers
But Can You Use It? Design Recommendations for Differentially Private Interactive Systems
Liudas Panavas, Joshua Snoke, Erika Tyagi +2
Accessing data collected by federal statistical agencies is essential for public policy research and improving evidence-based decision making, such as evaluating the effectiveness…
Incompatibilities Between Current Practices in Statistical Data Analysis and Differential Privacy
Joshua Snoke, Claire McKay Bowen, Aaron R. Williams +1
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 highligh…
Advancing Microdata Privacy Protection: A Review of Synthetic Data
Jingchen Hu, Claire McKay Bowen
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 g…
A Feasibility Study of Differentially Private Summary Statistics and Regression Analyses with Evaluations on Administrative and Survey Data
Andrés F. Barrientos, Aaron R. Williams, Joshua Snoke +1
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…
Comparative Study of Differentially Private Synthetic Data Algorithms from the NIST PSCR Differential Privacy Synthetic Data Challenge
Claire McKay Bowen, Joshua Snoke
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 u…
Differentially Private Data Release via Statistical Election to Partition Sequentially
Claire McKay Bowen, Fang Liu, Binyue Su
Differential Privacy (DP) formalizes privacy in mathematical terms and provides a robust concept for privacy protection. DIfferentially Private Data Synthesis (DIPS) techniques pro…