{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:41:31Z","timestamp":1760060491715,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T00:00:00Z","timestamp":1756425600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Austrian Federal Ministry of Economy, Energy and Tourism","award":["SBA-K1 NGC"],"award-info":[{"award-number":["SBA-K1 NGC"]}]},{"name":"National Foundation for Research, Technology and Development","award":["SBA-K1 NGC"],"award-info":[{"award-number":["SBA-K1 NGC"]}]},{"name":"Christian Doppler Research Association","award":["SBA-K1 NGC"],"award-info":[{"award-number":["SBA-K1 NGC"]}]},{"name":"BMIMI","award":["SBA-K1 NGC"],"award-info":[{"award-number":["SBA-K1 NGC"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/bdcc9090223","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T16:18:15Z","timestamp":1756484295000},"page":"223","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Data Obfuscation for Privacy-Preserving Machine Learning Using Quantum Symmetry Properties"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2206-9263","authenticated-orcid":false,"given":"Sebastian","family":"Raubitzek","sequence":"first","affiliation":[{"name":"SBA Research gGmbH, Floragasse 7\/5.OG, 1040 Vienna, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2115-2022","authenticated-orcid":false,"given":"Sebastian","family":"Schrittwieser","sequence":"additional","affiliation":[{"name":"Christian Doppler Laboratory for Assurance and Transparency in Software Protection, Faculty of Computer Science, University of Vienna, Kolingasse 14-16, 1090 Vienna, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander","family":"Schatten","sequence":"additional","affiliation":[{"name":"Institute of Information Systems Engineering, TU Wien, Favoritenstrasse 9-11\/194, 1040 Vienna, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kevin","family":"Mallinger","sequence":"additional","affiliation":[{"name":"Christian Doppler Laboratory for Assurance and Transparency in Software Protection, Faculty of Computer Science, University of Vienna, Kolingasse 14-16, 1090 Vienna, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/JSYST.2020.3024956","article-title":"A Review of Quantum Key Distribution Protocols in the Perspective of Smart Grid Communication Security","volume":"16","author":"Kong","year":"2022","journal-title":"IEEE Syst. 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