1 citations · 1 across the 4 of their papers we have counts for
4 papers
Privacy Amplification Persists under Unlimited Synthetic Data Release
Clément Pierquin, Aurélien Bellet, Marc Tommasi +1
We study privacy amplification by synthetic data release, a phenomenon in which differential privacy guarantees are improved by releasing only synthetic data rather than the privat…
Privacy Amplification Through Synthetic Data: Insights from Linear Regression
Clément Pierquin, Aurélien Bellet, Marc Tommasi +1
Synthetic data inherits the differential privacy guarantees of the model used to generate it. Additionally, synthetic data may benefit from privacy amplification when the generativ…
Practical considerations on using private sampling for synthetic data
Clément Pierquin, Bastien Zimmermann, Matthieu Boussard
Artificial intelligence and data access are already mainstream. One of the main challenges when designing an artificial intelligence or disclosing content from a database is preser…
Rényi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration
Clément Pierquin, Aurélien Bellet, Marc Tommasi +1
Pufferfish privacy is a flexible generalization of differential privacy that allows to model arbitrary secrets and adversary's prior knowledge about the data. Unfortunately, design…