2 papers
cs.LG2021
Not all noise is accounted equally: How differentially private learning benefits from large sampling rates
Friedrich Dörmann, Osvald Frisk, Lars Nørvang Andersen +1
Learning often involves sensitive data and as such, privacy preserving extensions to Stochastic Gradient Descent (SGD) and other machine learning algorithms have been developed usi…
cs.LG2021
Super-convergence and Differential Privacy: Training faster with better privacy guarantees
Osvald Frisk, Friedrich Dörmann, Christian Marius Lillelund +1
The combination of deep neural networks and Differential Privacy has been of increasing interest in recent years, as it offers important data protection guarantees to the individua…