1.1k citations · 2.1k across the 73 of their papers we have counts for
38 papers · 1 filter
Information Leakage Envelopes
Sara Saeidian, Carlos Pinzón, Catuscia Palamidessi
We study privacy guarantees in the framework of pointwise maximal leakage (PML) that satisfy two requirements: they are robust under post-processing and upper bound the failure pro…
Protection against Source Inference Attacks in Federated Learning
Andreas Athanasiou, Kangsoo Jung, Catuscia Palamidessi
Federated Learning (FL) was initially proposed as a privacy-preserving machine learning paradigm. However, FL has been shown to be susceptible to a series of privacy attacks. Recen…
Estimating the True Distribution of Data Collected with Randomized Response
Carlos Antonio Pinzón, Ehab ElSalamouny, Lucas Massot +3
Randomized Response (RR) is a protocol designed to collect and analyze categorical data with local differential privacy guarantees. It has been used as a building block of mechanis…
On the Consistency and Performance of the Iterative Bayesian Update
Ehab ElSalamouny, Catuscia Palamidessi
In many situations, estimating the distribution of users' data concerning certain attributes is important. To facilitate this estimation while safeguarding users' privacy, the loca…
Self-Defense: Optimal QIF Solutions and Application to Website Fingerprinting
Andreas Athanasiou, Konstantinos Chatzikokolakis, Catuscia Palamidessi
Quantitative Information Flow (QIF) provides a robust information-theoretical framework for designing secure systems with minimal information leakage. While previous research has a…
Protection against Source Inference Attacks in Federated Learning using Unary Encoding and Shuffling
Andreas Athanasiou, Kangsoo Jung, Catuscia Palamidessi
Federated Learning (FL) enables clients to train a joint model without disclosing their local data. Instead, they share their local model updates with a central server that moderat…