5 papers
Balancing Privacy-Quality-Efficiency in Federated Learning through Round-Based Interleaving of Protection Techniques
Yenan Wang, Carla Fabiana Chiasserini, Elad Michael Schiller
In federated learning (FL), balancing privacy protection, learning quality, and efficiency remains a challenge. Privacy protection mechanisms, such as Differential Privacy (DP), de…
Integrating Homomorphic Encryption and Synthetic Data in FL for Privacy and Learning Quality
Yenan Wang, Carla Fabiana Chiasserini, Elad Michael Schiller
Federated learning (FL) enables collaborative training of machine learning models without sharing sensitive client data, making it a cornerstone for privacy-critical applications.…
Towards a Formal Verification of Secure Vehicle Software Updates
Martin Slind Hagen, Emil Lundqvist, Alex Phu +3
With the rise of software-defined vehicles (SDVs), where software governs most vehicle functions alongside enhanced connectivity, the need for secure software updates has become in…
Self-Stabilizing Replicated State Machine Coping with Byzantine and Recurring Transient Faults
Shlomi Dolev, Amit Hendin, Maurice Herlihy +2
The ability to perform repeated Byzantine agreement lies at the heart of important applications such as blockchain price oracles or replicated state machines. Any such protocol req…
Near-Optimal Communication Byzantine Reliable Broadcast under a Message Adversary
Timothé Albouy, Davide Frey, Ran Gelles +5
We address the problem of Reliable Broadcast in asynchronous message-passing systems with nodes, of which up to are malicious (faulty), in addition to a message adversary t…