Work

Data Obfuscation for Privacy-Preserving Machine Learning Using Quantum Symmetry Properties

Year: 2025

Type: article

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.

Source: Big Data and Cognitive Computing

DOI: 10.3390/bdcc9090223

Authors: Sebastian Raubitzek, Alexander Schatten, Sebastian Schrittwieser, Kevin Mallinger

Institutions: SBA Research, University of Vienna, TU Wien

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,172

Funders: Österreichische Nationalstiftung für Forschung, Technologie und Entwicklung, Christian Doppler Forschungsgesellschaft