67 citations · 71 across the 6 of their papers we have counts for
6 papers · 1 filter
Verifiability and Privacy in Federated Learning through Context-Hiding Multi-Key Homomorphic Authenticators
Simone Bottoni, Giulio Zizzo, Stefano Braghin +1
Federated Learning has rapidly expanded from its original inception to now have a large body of research, several frameworks, and sold in a variety of commercial offerings. Thus, i…
DLPFS: The Data Leakage Prevention FileSystem
Stefano Braghin, Marco Simioni, Mathieu Sinn
Shared folders are still a common practice for granting third parties access to data files, regardless of the advances in data sharing technologies. Services like Google Drive, Dro…
Secure k-Anonymization over Encrypted Databases
Manish Kesarwani, Akshar Kaul, Stefano Braghin +2
Data protection algorithms are becoming increasingly important to support modern business needs for facilitating data sharing and data monetization. Anonymization is an important s…
Diffprivlib: The IBM Differential Privacy Library
Naoise Holohan, Stefano Braghin, Pól Mac Aonghusa +1
Since its conception in 2006, differential privacy has emerged as the de-facto standard in data privacy, owing to its robust mathematical guarantees, generalised applicability and…
The Bounded Laplace Mechanism in Differential Privacy
Naoise Holohan, Spiros Antonatos, Stefano Braghin +1
The Laplace mechanism is the workhorse of differential privacy, applied to many instances where numerical data is processed. However, the Laplace mechanism can return semantically…
(,)-Anonymity: -Anonymity with -Differential Privacy
Naoise Holohan, Spiros Antonatos, Stefano Braghin +1
The explosion in volume and variety of data offers enormous potential for research and commercial use. Increased availability of personal data is of particular interest in enabling…