25 citations · 30 across the 4 of their papers we have counts for
5 papers
Granularity is crucial when applying differential privacy to text: An investigation for neural machine translation
Doan Nam Long Vu, Timour Igamberdiev, Ivan Habernal
Applying differential privacy (DP) by means of the DP-SGD algorithm to protect individual data points during training is becoming increasingly popular in NLP. However, the choice o…
DP-NMT: Scalable Differentially-Private Machine Translation
Timour Igamberdiev, Doan Nam Long Vu, Felix Künnecke +3
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 sig…
DP-BART for Privatized Text Rewriting under Local Differential Privacy
Timour Igamberdiev, Ivan Habernal
Privatized text rewriting with local differential privacy (LDP) is a recent approach that enables sharing of sensitive textual documents while formally guaranteeing privacy protect…
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…
Privacy-Preserving Graph Convolutional Networks for Text Classification
Timour Igamberdiev, Ivan Habernal
Graph convolutional networks (GCNs) are a powerful architecture for representation learning on documents that naturally occur as graphs, e.g., citation or social networks. However,…