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cs.CR2026
A Security Framework for Chemical Functions
Frederik Walter, Hrishi Narayanan, Jessica Bariffi +5
In this paper, we introduce chemical functions, a unified framework that models chemical systems as noisy challenge--response primitives, and formalize the associated chemical func…
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…