84 citations · 214 across the 16 of their papers we have counts for
16 papers
Publishing Wikipedia usage data with strong privacy guarantees
Temilola Adeleye, Skye Berghel, Damien Desfontaines +9
For almost 20 years, the Wikimedia Foundation has been publishing statistics about how many people visited each Wikipedia page on each day. This data helps Wikipedia editors determ…
Tumult Analytics: a robust, easy-to-use, scalable, and expressive framework for differential privacy
Skye Berghel, Philip Bohannon, Damien Desfontaines +10
In this short paper, we outline the design of Tumult Analytics, a Python framework for differential privacy used at institutions such as the U.S. Census Bureau, the Wikimedia Found…
Precision-based attacks and interval refining: how to break, then fix, differential privacy on finite computers
Samuel Haney, Damien Desfontaines, Luke Hartman +2
Despite being raised as a problem over ten years ago, the imprecision of floating point arithmetic continues to cause privacy failures in the implementations of differentially priv…
Benchmarking Differentially Private Synthetic Data Generation Algorithms
Yuchao Tao, Ryan McKenna, Michael Hay +2
This work presents a systematic benchmark of differentially private synthetic data generation algorithms that can generate tabular data. Utility of the synthetic data is evaluated…
Differentially Private Algorithms for 2020 Census Detailed DHC Race \& Ethnicity
Sam Haney, William Sexton, Ashwin Machanavajjhala +2
This article describes a proposed differentially private (DP) algorithms that the US Census Bureau is considering to release the Detailed Demographic and Housing Characteristics (D…
HDMM: Optimizing error of high-dimensional statistical queries under differential privacy
Ryan McKenna, Gerome Miklau, Michael Hay +1
In this work we describe the High-Dimensional Matrix Mechanism (HDMM), a differentially private algorithm for answering a workload of predicate counting queries. HDMM represents qu…