67 citations · 84 across the 12 of their papers we have counts for
15 papers
Categorical Prior Lock-in: Why In-Context Learning Fails for Structured Data
Antonio Pelusi, Stefano Braghin, Alberto Trombetta
Large language models (LLMs) are increasingly used as conditional generators for structured data, relying on in-context learning (ICL) to adapt to new distributions without paramet…
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…
Robust Learning Protocol for Federated Tumor Segmentation Challenge
Ambrish Rawat, Giulio Zizzo, Swanand Kadhe +2
In this work, we devise robust and efficient learning protocols for orchestrating a Federated Learning (FL) process for the Federated Tumor Segmentation Challenge (FeTS 2022). Enab…
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…
Secure Random Sampling in Differential Privacy
Naoise Holohan, Stefano Braghin
Differential privacy is among the most prominent techniques for preserving privacy of sensitive data, oweing to its robust mathematical guarantees and general applicability to a va…
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…
AnonTokens: tracing re-identification attacks through decoy records
Spiros Antonatos, Stefano Braghin, Naoise Holohan +1
Privacy is of the utmost concern when it comes to releasing data to third parties. Data owners rely on anonymization approaches to safeguard the released datasets against re-identi…
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…
Predicting User Engagement in Twitter with Collaborative Ranking
Ernesto Diaz-Aviles, Hoang Thanh Lam, Fabio Pinelli +4
Collaborative Filtering (CF) is a core component of popular web-based services such as Amazon, YouTube, Netflix, and Twitter. Most applications use CF to recommend a small set of i…
Answering queries using pairings
Alberto Trombetta, Giuseppe Persiano, Stefano Braghin
Outsourcing data in the cloud has become nowadays very common. Since -- generally speaking -- cloud data storage and management providers cannot be fully trusted, mechanisms provid…
Secure and Policy-Private Resource Sharing in an Online Social Network
Stefano Braghin, Vincenzo Iovino, Giuseppe Persiano +1
Providing functionalities that allow online social network users to manage in a secure and private way the publication of their information and/or resources is a relevant and far f…
The Zen of Multidisciplinary Team Recommendation
Anwitaman Datta, Stefano Braghin, Jackson Tan Teck Yong
In order to accomplish complex tasks, it is often necessary to compose a team consisting of experts with diverse competencies. However, for proper functioning, it is also preferabl…
PriSM: A Private Social Mesh for Leveraging Social Networking at Workplace
Stefano Braghin, Jackson Tan, Rajesh Sharma +1
In this work we describe the PriSM framework for decentralized deployment of a federation of autonomous social networks (ASN). The individual ASNs are centrally managed by organiza…