3 papers
cs.CR2025
Composition Theorems for f-Differential Privacy
Natasha Fernandes, Annabelle McIver, Parastoo Sadeghi
"f differential privacy" (fDP) is a recent definition for privacy privacy which can offer improved predictions of "privacy loss". It has been used to analyse specific privacy mecha…
cs.LG2025
Empirical Calibration and Metric Differential Privacy in Language Models
Pedro Faustini, Natasha Fernandes, Annabelle McIver +1
NLP models trained with differential privacy (DP) usually adopt the DP-SGD framework, and privacy guarantees are often reported in terms of the privacy budget . However, doe…
cs.LG2025
Comparing privacy notions for protection against reconstruction attacks in machine learning
Sayan Biswas, Mark Dras, Pedro Faustini +4
Within the machine learning community, reconstruction attacks are a principal concern and have been identified even in federated learning (FL), which was designed with privacy pres…