Version 1
: Received: 22 July 2024 / Approved: 22 July 2024 / Online: 23 July 2024 (13:39:59 CEST)
How to cite:
Raubitzek, S.; Schatten, A.; Schrittwieser, S.; Mallinger, K. Data Obfuscation for Privacy-Preserving Machine Learning using Quantum Symmetry Properties. Preprints2024, 2024071701. https://doi.org/10.20944/preprints202407.1701.v1
Raubitzek, S.; Schatten, A.; Schrittwieser, S.; Mallinger, K. Data Obfuscation for Privacy-Preserving Machine Learning using Quantum Symmetry Properties. Preprints 2024, 2024071701. https://doi.org/10.20944/preprints202407.1701.v1
Raubitzek, S.; Schatten, A.; Schrittwieser, S.; Mallinger, K. Data Obfuscation for Privacy-Preserving Machine Learning using Quantum Symmetry Properties. Preprints2024, 2024071701. https://doi.org/10.20944/preprints202407.1701.v1
APA Style
Raubitzek, S., Schatten, A., Schrittwieser, S., & Mallinger, K. (2024). Data Obfuscation for Privacy-Preserving Machine Learning using Quantum Symmetry Properties. Preprints. https://doi.org/10.20944/preprints202407.1701.v1
Chicago/Turabian Style
Raubitzek, S., Sebastian Schrittwieser and Kevin Mallinger. 2024 "Data Obfuscation for Privacy-Preserving Machine Learning using Quantum Symmetry Properties" Preprints. https://doi.org/10.20944/preprints202407.1701.v1
Abstract
This study introduces a data obfuscation technique, leveraging the exponential map associated with the generators of Lie groups. Originating from quantum machine learning frameworks, our method illustrates the practical application of quantum mechanics principles in data processing. Specifically, it employs the exponential map of a generator algebra to introduce controlled noise into the data, achieving obfuscated data while preserving its utility for machine learning tasks. This strategy is shown to safeguard privacy in sensitive datasets, such as discussed medical records, and to enhance dataset volume and diversity through augmentation. Our empirical analysis, benchmarked against standard machine learning approaches, demonstrates that our method can maintain or even improve the predictive accuracy of the original data. This research highlights the potential of Lie group theory for advancing data privacy in medicine, marking a significant contribution to machine learning methodologies by offering the dual benefits of data obfuscation and enrichment. Through this synthesis of algebraic structures and machine learning, we propose new pathways for the secure and effective use of data in sensitive areas.
Keywords
Data Obfuscation; Machine Learning; Boost Classifier; Medical Data; Diabetes; Breast Cancer; Artificial Intelligence; Data Privacy; Quantum Machine Learning; Quantum Information Processing
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
