108 citations · 135 across the 5 of their papers we have counts for
7 papers
MM: A general method to perform various data analysis tasks from a differentially private sketch
Florimond Houssiau, Vincent Schellekens, Antoine Chatalic +2
Differential privacy is the standard privacy definition for performing analyses over sensitive data. Yet, its privacy budget bounds the number of tasks an analyst can perform with…
A Framework for Auditable Synthetic Data Generation
Florimond Houssiau, Samuel N. Cohen, Lukasz Szpruch +5
Synthetic data has gained significant momentum thanks to sophisticated machine learning tools that enable the synthesis of high-dimensional datasets. However, many generation techn…
TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data
Florimond Houssiau, James Jordon, Samuel N. Cohen +6
Personal data collected at scale promises to improve decision-making and accelerate innovation. However, sharing and using such data raises serious privacy concerns. A promising so…
QuerySnout: Automating the Discovery of Attribute Inference Attacks against Query-Based Systems
Ana-Maria Cretu, Florimond Houssiau, Antoine Cully +1
Although query-based systems (QBS) have become one of the main solutions to share data anonymously, building QBSes that robustly protect the privacy of individuals contributing to…
Synthetic Data -- what, why and how?
James Jordon, Lukasz Szpruch, Florimond Houssiau +5
This explainer document aims to provide an overview of the current state of the rapidly expanding work on synthetic data technologies, with a particular focus on privacy. The artic…
When the signal is in the noise: Exploiting Diffix's Sticky Noise
Andrea Gadotti, Florimond Houssiau, Luc Rocher +2
Anonymized data is highly valuable to both businesses and researchers. A large body of research has however shown the strong limits of the de-identification release-and-forget mode…