2 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
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…