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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…
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…
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…
A Systematic and Formal Study of the Impact of Local Differential Privacy on Fairness: Preliminary Results
Karima Makhlouf, Tamara Stefanovic, Heber H. Arcolezi +1
Machine learning (ML) algorithms rely primarily on the availability of training data, and, depending on the domain, these data may include sensitive information about the data prov…
On the Impact of Multi-dimensional Local Differential Privacy on Fairness
Karima Makhlouf, Heber H. Arcolezi, Sami Zhioua +2
Automated decision systems are increasingly used to make consequential decisions in people's lives. Due to the sensitivity of the manipulated data as well as the resulting decision…