Oct 18, 2017 · We find that our private LSTM language models are quantitatively and qualitatively similar to un-noised models when trained on a large dataset.

This work builds on recent advances in the training of deep networks on user-partitioned data and privacy accounting for stochastic gradient descent, ...

Sep 10, 2024 · We find that private LSTM language models are quantitatively and qualitatively similar to un-noised models when trained on a large dataset.

We demonstrate the first high quality LSTM language model trained with strong privacy guar- antees in §3, showing no significant decrease in model accuracy ...

Feb 24, 2018 · We demonstrate that it is possible to train large recurrent language models with user-level differential privacy guarantees with only a ...

Oct 18, 2017 · We demonstrate that it is possible to train large recurrent language modelswith user-level differential privacy guarantees without ...

This work builds on recent advances in the training of deep networks on user-partitioned data and privacy accounting for stochastic gradient descent.

Training neural models with differential privacy often significantly degrades model performance. However, differential privacy could prove crucial when doing ...

Differential privacy (DP) is a popular mechanism for training machine learning models with bounded leakage about the presence of specific points in the ...