3 citations · 5 across the 7 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…
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…
pMSE Mechanism: Differentially Private Synthetic Data with Maximal Distributional Similarity
Joshua Snoke, Aleksandra Slavković
We propose a method for the release of differentially private synthetic datasets. In many contexts, data contain sensitive values which cannot be released in their original form in…
Providing Accurate Models across Private Partitioned Data: Secure Maximum Likelihood Estimation
Joshua Snoke, Timothy R. Brick, Aleksandra Slavkovic +1
This paper focuses on the privacy paradigm of providing access to researchers to remotely carry out analyses on sensitive data stored behind firewalls. We address the situation whe…
Using Neural Generative Models to Release Synthetic Twitter Corpora with Reduced Stylometric Identifiability of Users
Alexander G. Ororbia, Fridolin Linder, Joshua Snoke
We present a method for generating synthetic versions of Twitter data using neural generative models. The goal is protecting individuals in the source data from stylometric re-iden…
General and specific utility measures for synthetic data
Joshua Snoke, Gillian Raab, Beata Nowok +2
Data holders can produce synthetic versions of datasets when concerns about potential disclosure restrict the availability of the original records. This paper is concerned with met…