Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning
Artificial intelligence, machine learning, and deep learning as a service have become the status quo for many industries, leading to the widespread deployment of models that handle sensitive data. Well-performing models, the industry seeks, usually re
Authors
- Iliana Loi (ORCID: https://orcid.org/0000-0001-9112-0638)
- Ioannis Symeonidis (ORCID: https://orcid.org/0000-0002-5050-8617)
- Κωνσταντίνος Μουστάκας (ORCID: https://orcid.org/0000-0002-1721-7121)
- Aristotelis Spiliotis (ORCID: https://orcid.org/0000-0001-9042-2044)
- Μαρία Πάνου (ORCID: https://orcid.org/0000-0002-2161-9246)
- Nikolaos Chandrinos (ORCID: https://orcid.org/0009-0004-0737-3261)
- Panagiotis Zachos
Institutions
- University of Pannonia (HU)
Publication Details
- Published
- 2025-08-25
- DOI
- https://doi.org/10.22541/au.175610020.05913630/v1
- Citations
- 1
- Primary Topic
- Privacy-Preserving Technologies in Data
- Type
- article
- Field-Weighted Citation Impact
- 2.48
Funders
- European Commission
- HORIZON EUROPE Framework Programme