2 papers
cs.CR2024
Protecting Privacy in Federated Time Series Analysis: A Pragmatic Technology Review for Application Developers
Daniel Bachlechner, Ruben Hetfleisch, Stephan Krenn +2
The federated analysis of sensitive time series has huge potential in various domains, such as healthcare or manufacturing. Yet, to fully unlock this potential, requirements impose…
cs.CR2023
HE-MAN -- Homomorphically Encrypted MAchine learning with oNnx models
Martin Nocker, David Drexel, Michael Rader +2
Machine learning (ML) algorithms are increasingly important for the success of products and services, especially considering the growing amount and availability of data. This also…