RT journal article
T1 A Privacy-Preserving Behavioral Authentication Framework for Public Transport Applications
A1 Monschein, David
A1 Knecht, Markus
A1 Al Samara, Mustafa
A1 Peregrina, José Antonio
A1 Piotrowski, Tim
A1 Gohlke, Sascha
A1 Bennis, Ismail
A1 Azan, Wilfrid
A1 Abouaissa, Abdelhafid
A1 García de Prado Fontela, Alfonso
A1 Gilg, Marc
A1 Neumann, Eva Maria
A1 Nochta, Zoltán
A1 Ortiz Bellot, Guadalupe
A1 Szilagyi, Ioan
A1 Waldhorst, Oliver P.
A1 Zirpins, Christian
A2 Ingeniería en AutomáticaElectrónica, Arquitectura y Redes de Computadores
A2 Ingeniería Informática
K1 Behavioral Authentication
K1 Behavioral Biometrics
K1 Privacy-Preserving Machine Learning
K1 Real-World Deployment
K1 Federated Learning
K1 Homomorphic Encryption
K1 Authentication Infrastructure
AB Public transportation systems increasingly rely on mobile services for ticketing, trip planning, and account management. These systems require secure and user-friendly authentication because traditional login mechanisms often disrupt the travel experience and only protect the initial login. In this context, behavioral authentication based on interaction patterns and sensor data offers a promising solution for continuous user verification. However, its practical deployment raises challenges related to privacy, robustness, distributed model training, and integration with existing authentication infrastructures. This paper presents the design and deployment of a privacy-preserving behavioral authentication framework for public transportation applications. The framework focuses on system-level integration of established privacy-preserving machine learning and authentication techniques. It combines client-side preprocessing and homomorphic encryption of behavioral data with server-side machine learning-based analysis. It further incorporates model optimization techniques to improve robustness against impersonation and supports collaborative model training across providers through federated learning with coordinated preprocessing and data governance. To enable practical deployment, the framework integrates behavioral authentication into existing OAuth 2.0 and OpenID Connect-based infrastructures, including authentication flows, token management, and cross-provider interoperability.We evaluated the proposed framework through preliminary experiments on a public dataset, a field test with a real-world public transport application, and a second field test exploring federated learning in a collaborative training scenario. The results demonstrate the practical feasibility of the proposed architecture and provide insights into the challenges of deploying behavioral authentication in real-world mobility ecosystems.
PB IEEE (Institute of Electrical and Electronics Engineers)
SN 2169-3536
YR 2026
FD 2026-06-02
LK http://hdl.handle.net/10498/39748
UL http://hdl.handle.net/10498/39748
LA eng
NO Interreg Upper Rhine program (European Union)
DS Repositorio Institucional de la Universidad de Cádiz
RD 05-nov-2026