Search Results for author:
Found 5 papers, 2 papers with code
Controllable Synthetic Clinical Note Generation with Privacy Guarantees
We conduct utility testing to evaluate the performance of machine learning models trained on the cloned datasets.
Investigating the Effect of Misalignment on Membership Privacy in the White-box Setting
We here present the first systematic analysis of the causes of misalignment in shadow models and show the use of a different weight initialisation to be the main cause.
Bayesian Estimation of Differential Privacy
Our Bayesian method exploits the hypothesis testing interpretation of differential privacy to obtain a posterior for $\varepsilon$ (not just a confidence interval) from the joint posterior of the false positive and false negative rates of membership inference attacks.
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.
