23 citations · 58 across the 4 of their papers we have counts for
5 papers
Differentially private significance tests for regression coefficients
Andrés F. Barrientos, Jerome P. Reiter, Ashwin Machanavajjhala +1
Many data producers seek to provide users access to confidential data without unduly compromising data subjects' privacy and confidentiality. One general strategy is to require use…
Providing Access to Confidential Research Data Through Synthesis and Verification: An Application to Data on Employees of the U.S. Federal Government
Andrés F. Barrientos, Alexander Bolton, Tom Balmat +6
Data stewards seeking to provide access to large-scale social science data face a difficult challenge. They have to share data in ways that protect privacy and confidentiality, are…
Principled Evaluation of Differentially Private Algorithms using DPBench
Michael Hay, Ashwin Machanavajjhala, Gerome Miklau +2
Differential privacy has become the dominant standard in the research community for strong privacy protection. There has been a flood of research into query answering algorithms th…
On the Privacy Properties of Variants on the Sparse Vector Technique
Yan Chen, Ashwin Machanavajjhala
The sparse vector technique is a powerful differentially private primitive that allows an analyst to check whether queries in a stream are greater or lesser than a threshold. This…
Differentially Private Algorithms for Empirical Machine Learning
Ben Stoddard, Yan Chen, Ashwin Machanavajjhala
An important use of private data is to build machine learning classifiers. While there is a burgeoning literature on differentially private classification algorithms, we find that…