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Privacy Regularization: Joint Privacy-Utility Optimization in Language Models
In this work, we introduce two privacy-preserving regularization methods for training language models that enable joint optimization of utility and privacy through (1) the use of a discriminator and (2) the inclusion of a triplet-loss term.
Training Data Leakage Analysis in Language Models
It has been demonstrated that strong performance of language models comes along with the ability to memorize rare training samples, which poses serious privacy threats in case the model is trained on confidential user content.
Analyzing Information Leakage of Updates to Natural Language Models
To continuously improve quality and reflect changes in data, machine learning applications have to regularly retrain and update their core models.
