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…