12 citations · 14 across the 23 of their papers we have counts for
4 papers · 1 filter
Post-Quantum Secure Aggregation via Code-Based Homomorphic Encryption
Sebastian Bitzer, Maximilian Egger, Mumin Liu +1
Secure aggregation lets a server learn the sum of inputs from multiple users without learning individual contributions. Recent single-server protocols achieve this with key- and me…
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…
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…
Sparsity and Privacy in Secret Sharing: A Fundamental Trade-Off
Rawad Bitar, Maximilian Egger, Antonia Wachter-Zeh +1
This work investigates the design of sparse secret sharing schemes that encode a sparse private matrix into sparse shares. This investigation is motivated by distributed computing,…