197 citations · 424 across the 13 of their papers we have counts for
14 papers · 1 filter
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…
Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment
Rachel Cummings, Damien Desfontaines, David Evans +21
In this article, we present a detailed review of current practices and state-of-the-art methodologies in the field of differential privacy (DP), with a focus of advancing DP's depl…
DP-SIPS: A simpler, more scalable mechanism for differentially private partition selection
Marika Swanberg, Damien Desfontaines, Samuel Haney
Partition selection, or set union, is an important primitive in differentially private mechanism design: in a database where each user contributes a list of items, the goal is to p…
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…
Google COVID-19 Vaccination Search Insights: Anonymization Process Description
Shailesh Bavadekar, Adam Boulanger, John Davis +22
This report describes the aggregation and anonymization process applied to the COVID-19 Vaccination Search Insights (published at http://goo.gle/covid19vaccinationinsights), a publ…
A General Purpose Transpiler for Fully Homomorphic Encryption
Shruthi Gorantala, Rob Springer, Sean Purser-Haskell +22
Fully homomorphic encryption (FHE) is an encryption scheme which enables computation on encrypted data without revealing the underlying data. While there have been many advances in…
Google COVID-19 Search Trends Symptoms Dataset: Anonymization Process Description (version 1.0)
Shailesh Bavadekar, Andrew Dai, John Davis +27
This report describes the aggregation and anonymization process applied to the initial version of COVID-19 Search Trends symptoms dataset (published at https://goo.gle/covid19sympt…
Differentially private partition selection
Damien Desfontaines, James Voss, Bryant Gipson +1
Many data analysis operations can be expressed as a GROUP BY query on an unbounded set of partitions, followed by a per-partition aggregation. To make such a query differentially p…
Google COVID-19 Community Mobility Reports: Anonymization Process Description (version 1.1)
Ahmet Aktay, Shailesh Bavadekar, Gwen Cossoul +21
This document describes the aggregation and anonymization process applied to the initial version of Google COVID-19 Community Mobility Reports (published at http://google.com/covid…
Differentially Private SQL with Bounded User Contribution
Royce J Wilson, Celia Yuxin Zhang, William Lam +3
Differential privacy (DP) provides formal guarantees that the output of a database query does not reveal too much information about any individual present in the database. While ma…
SoK: Differential Privacies
Damien Desfontaines, Balázs Pejó
Shortly after it was first introduced in 2006, differential privacy became the flagship data privacy definition. Since then, numerous variants and extensions were proposed to adapt…
Differential privacy with partial knowledge
Damien Desfontaines, Esfandiar Mohammadi, Elisabeth Krahmer +1
Differential privacy offers formal quantitative guarantees for algorithms over datasets, but it assumes attackers that know and can influence all but one record in the database. Th…
Cardinality Estimators do not Preserve Privacy
Damien Desfontaines, Andreas Lochbihler, David Basin
Cardinality estimators like HyperLogLog are sketching algorithms that estimate the number of distinct elements in a large multiset. Their use in privacy-sensitive contexts raises t…