Joshua Snoke, Ph.D.
Joshua Snoke, Ph.D.
Associate Statistician, RAND Corporation
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General and specific utility measures for synthetic data
J Snoke, GM Raab, B Nowok, C Dibben, A Slavkovic
Journal of the Royal Statistical Society: Series A (Statistics in Society …, 2018
pMSE Mechanism: Differentially Private Synthetic Data with Maximal Distributional Similarity
J Snoke, A Slavković
International Conference on Privacy in Statistical Databases, 138-159, 2018
Providing accurate models across private partitioned data: Secure maximum likelihood estimation
J Snoke, TR Brick, A Slavković, MD Hunter
The Annals of Applied Statistics 12 (2), 877-914, 2018
synthpop: Generating synthetic versions of sensitive microdata for statistical disclosure control
B Nowok, GM Raab, J Snoke, C Dibben
R package version, 1.3-0, 2016
Using Neural Generative Models to Release Synthetic Twitter Corpora with Reduced Stylometric Identifiability of Users
AG Ororbia II, F Linder, J Snoke
arXiv preprint arXiv:1606.01151, 2017
Accurate Estimation of Structural Equation Models with Remote Partitioned Data
J Snoke, T Brick, A Slavković
International Conference on Privacy in Statistical Databases, 190-209, 2016
Comparative Study of Differentially Private Synthetic Data Algorithms and Evaluation Standards
C McKay Bowen, J Snoke
arXiv, arXiv: 1911.12704, 2019
Package ‘synthpop’
B Nowok, GM Raab, J Snoke, C Dibben, MB Nowok
Differential Privacy: What Is It?
J Snoke, CMK Bowen
AMSTAT news: the membership magazine of the American Statistical Association …, 2019
How Statisticians Should Grapple with Privacy in a Changing Data Landscape
J Snoke, CMK Bowen
CHANCE 33 (4), 6-13, 2020
Statistical Data Privacy Methods for Increasing Research Opportunities
JV Snoke
The Pennsylvania State University, 2018
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Artiklar 1–11