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Found 6 papers, 1 papers with code
About the Cost of Central Privacy in Density Estimation
We recover the result of Barber \& Duchi (2014) stating that histogram estimators are optimal against Lipschitz distributions for the L2 risk, and under regular differential privacy, and we extend it to other norms and notions of privacy.
Sparsity in neural networks can improve their privacy
This article measures how sparsity can make neural networks more robust to membership inference attacks.
Sparsity in neural networks can increase their privacy
This article measures how sparsity can make neural networks more robust to membership inference attacks.
Private Statistical Estimation of Many Quantiles
The first one consists in privately estimating the empirical quantiles of the samples and using this result as an estimator of the quantiles of the distribution.
On the Statistical Complexity of Estimation and Testing under Privacy Constraints
In certain scenarios, we show that maintaining privacy results in a noticeable reduction in performance only when the level of privacy protection is very high.
Extraction of Nystagmus Patterns from Eye-Tracker Data with Convolutional Sparse Coding
The analysis of the Nystagmus waveforms from eye-tracking records is crucial for the clinicial interpretation of this pathological movement.
