privacy

A summary report of methods to gauge the suitability and privacy of synthetic datasets, produced for the Alan Turing Institute’s Data Study Group programme.

Large pre-trained language models have pushed the boundaries of the state-of-the-art in NLP, but their use runs the risk of encoding unwanted private personal information derived from input texts. Our proposed system adds calibrated noise and an adversarial training objective to reduce private information leakage.