Sebastian Raubitzek in Big Data and Cognitive Computing
Our colleague Sebastian Raubitzek, researcher at SBA Research and a member of the Security and Privacy Research Group at the University of Vienna, has published a journal article titled “Data Obfuscation for Privacy-Preserving Machine Learning Using Quantum Symmetry Properties” in MDPI’s Journal Big Data and Cognitive Computing in collaboration with the CD lab AsTra.
© Niklas Schnaubelt
Abstract
This study introduces a data obfuscation technique that leverages the exponential map of Lie-group generators. Originating from quantum machine learning frameworks, the method injects controlled noise into these generators, deliberately breaking symmetry and obscuring the source data while retaining predictive utility. Experiments on open medical datasets show that classifiers trained on obfuscated features match or slightly exceed the baseline accuracy obtained on raw data. This work demonstrates how Lie-group theory can advance privacy in sensitive domains by providing simultaneous data obfuscation and augmentation.
Authors: Sebastian Raubitzek, Sebastian Schrittwieser, Alexander Schatten, and Kevin Mallinger.