3 papers
stat.AP2019
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…
stat.ME2018
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…
stat.ME2017
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…