11 citations · 12 across the 6 of their papers we have counts for
6 papers
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…
Mitigating Membership Inference Vulnerability in Personalized Federated Learning
Kangsoo Jung, Sayan Biswas, Catuscia Palamidessi
Federated Learning (FL) has emerged as a promising paradigm for collaborative model training without the need to share clients' personal data, thereby preserving privacy. However,…
Metric Privacy in Federated Learning for Medical Imaging: Improving Convergence and Preventing Client Inference Attacks
Judith Sáinz-Pardo Díaz, Andreas Athanasiou, Kangsoo Jung +2
Federated learning is a distributed learning technique that allows training a global model with the participation of different data owners without the need to share raw data. This…
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…
Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond
Filippo Galli, Kangsoo Jung, Sayan Biswas +2
Federated learning (FL) is a framework for training machine learning models in a distributed and collaborative manner. During training, a set of participating clients process their…