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Found 6 papers, 5 papers with code
DP-NMT: Scalable Differentially-Private Machine Translation
Neural machine translation (NMT) is a widely popular text generation task, yet there is a considerable research gap in the development of privacy-preserving NMT models, despite significant data privacy concerns for NMT systems.
DP-BART for Privatized Text Rewriting under Local Differential Privacy
Privatized text rewriting with local differential privacy (LDP) is a recent approach that enables sharing of sensitive textual documents while formally guaranteeing privacy protection to individuals.
DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewriting
Text rewriting with differential privacy (DP) provides concrete theoretical guarantees for protecting the privacy of individuals in textual documents.
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.
Privacy-Preserving Graph Convolutional Networks for Text Classification
Graph convolutional networks (GCNs) are a powerful architecture for representation learning on documents that naturally occur as graphs, e. g., citation or social networks.
