5 papers
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,…
Comparing privacy notions for protection against reconstruction attacks in machine learning
Sayan Biswas, Mark Dras, Pedro Faustini +4
Within the machine learning community, reconstruction attacks are a principal concern and have been identified even in federated learning (FL), which was designed with privacy pres…
Federated and differentially private estimation of KL divergence
Mary Scott, Sayan Biswas, Graham Cormode +1
Measuring distribution drifts is a key task in managing distributed, sensitive data, as it underpins a wide range of federated learning and analytics applications. In many practica…
Bayes' capacity as a measure for reconstruction attacks in federated learning
Sayan Biswas, Mark Dras, Pedro Faustini +4
Within the machine learning community, reconstruction attacks are a principal attack of concern and have been identified even in federated learning, which was designed with privacy…
Tight Differential Privacy Guarantees for the Shuffle Model with -Randomized Response
Sayan Biswas, Kangsoo Jung, Catuscia Palamidessi
Most differentially private (DP) algorithms assume a central model in which a reliable third party inserts noise to queries made on datasets, or a local model where the users local…