11 citations · 12 across the 6 of their papers we have counts for
3 papers · 1 filter
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…
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…
Causal Discovery Under Local Privacy
Rūta Binkytė, Carlos Pinzón, Szilvia Lestyán +3
Differential privacy is a widely adopted framework designed to safeguard the sensitive information of data providers within a data set. It is based on the application of controlled…