47 citations · 145 across the 24 of their papers we have counts for
5 papers · 1 filter
Differentially Private Fine-tuning of Language Models
Da Yu, Saurabh Naik, Arturs Backurs +9
We give simpler, sparser, and faster algorithms for differentially private fine-tuning of large-scale pre-trained language models, which achieve the state-of-the-art privacy versus…
Membership Inference on Word Embedding and Beyond
Saeed Mahloujifar, Huseyin A. Inan, Melissa Chase +2
In the text processing context, most ML models are built on word embeddings. These embeddings are themselves trained on some datasets, potentially containing sensitive data. In som…
On Privacy and Confidentiality of Communications in Organizational Graphs
Masoumeh Shafieinejad, Huseyin Inan, Marcello Hasegawa +1
Machine learned models trained on organizational communication data, such as emails in an enterprise, carry unique risks of breaching confidentiality, even if the model is intended…
Privacy Regularization: Joint Privacy-Utility Optimization in Language Models
Fatemehsadat Mireshghallah, Huseyin A. Inan, Marcello Hasegawa +3
Neural language models are known to have a high capacity for memorization of training samples. This may have serious privacy implications when training models on user content such…
Training Data Leakage Analysis in Language Models
Huseyin A. Inan, Osman Ramadan, Lukas Wutschitz +4
Recent advances in neural network based language models lead to successful deployments of such models, improving user experience in various applications. It has been demonstrated t…