Papers with Confidentially Redacted Training
Provably Confidential Language Modelling (2022.naacl-main)
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| Challenge: | Existing methods to train language models without memorizing sensitive data are mismatched and can be difficult to screen and filter. |
| Approach: | They propose a method to train language generation models while protecting the confidential segments of training data. |
| Outcome: | The proposed method prevents unintended memorization by randomizing parts of the training process while protecting strong confidentiality. |