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cs.CR2026
Differential Zonotopes for Verifying Global Robustness of DNNs
Anagha Athavale, Samuel Teuber, Matteo Maffei +3
The robustness of deep neural networks (DNNs) is critical in security-sensitive applications, where small input perturbations should not alter model predictions. This property is c…
cs.CR2026
Encrypted Neural Networks without Overflows
Philipp Kern, Lorenzo Rovida, Samuel Teuber +3
The popular Cheon-Kim-Kim-Song (CKKS) scheme enables efficient private inference in neural networks by evaluating them on encrypted data. Since CKKS only supports addition, multipl…
cs.CR2025
An Information-Flow Perspective on Algorithmic Fairness
Samuel Teuber, Bernhard Beckert
This work presents insights gained by investigating the relationship between algorithmic fairness and the concept of secure information flow. The problem of enforcing secure inform…