4 papers
About the Cost of Central Privacy in Density Estimation
Clément Lalanne, Aurélien Garivier, Rémi Gribonval
We study non-parametric density estimation for densities in Lipschitz and Sobolev spaces, and under central privacy. In particular, we investigate regimes where the privacy budget…
On the Statistical Complexity of Estimation and Testing under Privacy Constraints
Clément Lalanne, Aurélien Garivier, Rémi Gribonval
The challenge of producing accurate statistics while respecting the privacy of the individuals in a sample is an important area of research. We study minimax lower bounds for class…
Sparsity in neural networks can improve their privacy
Antoine Gonon, Léon Zheng, Clément Lalanne +3
This article measures how sparsity can make neural networks more robust to membership inference attacks. The obtained empirical results show that sparsity improves the privacy of t…
Can sparsity improve the privacy of neural networks?
Antoine Gonon, Léon Zheng, Clément Lalanne +3
Sparse neural networks are mainly motivated by ressource efficiency since they use fewer parameters than their dense counterparts but still reach comparable accuracies. This articl…