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cs.CR2026
Post-Quantum Secure Aggregation via Code-Based Homomorphic Encryption
Sebastian Bitzer, Maximilian Egger, Mumin Liu +1
Secure aggregation enables aggregation of inputs from multiple parties without revealing individual contributions to the server or other clients. Existing post-quantum approaches b…
cs.CR2025
Source Anonymity for Private Random Walk Decentralized Learning
Maximilian Egger, Svenja Lage, Rawad Bitar +1
This paper considers random walk-based decentralized learning, where at each iteration of the learning process, one user updates the model and sends it to a randomly chosen neighbo…
cs.CR2025
Federated One-Shot Learning with Data Privacy and Objective-Hiding
Maximilian Egger, Rüdiger Urbanke, Rawad Bitar
Privacy in federated learning is crucial, encompassing two key aspects: safeguarding the privacy of clients' data and maintaining the privacy of the federator's objective from the…