Manuel Senge

One size does not fit all: Investigating strategies for differentially-private learning across NLP tasks

Preserving privacy in contemporary NLP models allows us to work with sensitive data, but unfortunately comes at a price. We know that stricter privacy guarantees in differentially-private stochastic gradient descent (DP-SGD) generally degrade model performance. However, previous research on the effi…