4 citations · 5 across the 3 of their papers we have counts for
3 papers
Trade-Offs Between Fairness and Privacy in Language Modeling
Cleo Matzken, Steffen Eger, Ivan Habernal
Protecting privacy in contemporary NLP models is gaining in importance. So does the need to mitigate social biases of such models. But can we have both at the same time? Existing r…
Crowdsourcing on Sensitive Data with Privacy-Preserving Text Rewriting
Nina Mouhammad, Johannes Daxenberger, Benjamin Schiller +1
Most tasks in NLP require labeled data. Data labeling is often done on crowdsourcing platforms due to scalability reasons. However, publishing data on public platforms can only be…
DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewriting
Timour Igamberdiev, Thomas Arnold, Ivan Habernal
Text rewriting with differential privacy (DP) provides concrete theoretical guarantees for protecting the privacy of individuals in textual documents. In practice, existing systems…