466 citations · 1.6k across the 73 of their papers we have counts for
5 papers · 2 filters
Answering Private Linear Queries Adaptively using the Common Mechanism
Yingtai Xiao, Guanhong Wang, Danfeng Zhang +1
When analyzing confidential data through a privacy filter, a data scientist often needs to decide which queries will best support their intended analysis. For example, an analyst m…
Reconstruction Attacks on Aggressive Relaxations of Differential Privacy
Prottay Protivash, John Durrell, Zeyu Ding +2
Differential privacy is a widely accepted formal privacy definition that allows aggregate information about a dataset to be released while controlling privacy leakage for individua…
Bayesian and Frequentist Semantics for Common Variations of Differential Privacy: Applications to the 2020 Census
Daniel Kifer, John M. Abowd, Robert Ashmead +5
The purpose of this paper is to guide interpretation of the semantic privacy guarantees for some of the major variations of differential privacy, which include pure, approximate, R…
Geographic Spines in the 2020 Census Disclosure Avoidance System
Ryan Cumings-Menon, John M. Abowd, Robert Ashmead +5
The 2020 Census Disclosure Avoidance System (DAS) is a formally private mechanism that first adds independent noise to cross tabulations for a set of pre-specified hierarchical geo…
Exact Privacy Analysis of the Gaussian Sparse Histogram Mechanism
Brian Karrer, Daniel Kifer, Arjun Wilkins +1
Sparse histogram methods can be useful for returning differentially private counts of items in large or infinite histograms, large group-by queries, and more generally, releasing a…