Framework Design for Privacy Preserving Machine Learning in a 6G Vehicular Network

Date

2027-01-01

Author

Ucar, Alper Umut
Yayla, Oğuz

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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The transition to sixth-generation telecommunications introduces a paradigm shift in vehicular networks, enabling hyper-connected autonomous driving through advanced machine learning. However, training these models requires vast amounts of sensitive telemetry data, creating a conflict between data utility and user privacy. This paper addresses this trade-off by proposing a dual-framework architecture. First, we introduce a soft privacy framework, leveraging attribute-based encryption and proxy re-encryption to achieve ultra-low latency suitable for safety-critical applications like collision avoidance. Second, we present a hard privacy framework, utilizing homomorphic encryption, multi-party computation, and zero-knowledge proofs to establish a zero-trust environment for long-term statistical analysis. We provide a rigorous security analysis demonstrating resilience against collusion and data poisoning attacks. Furthermore, a theoretical performance evaluation compares computational complexities to confirm that while soft privacy optimizes for real-time actuation, hard privacy provides necessary information-theoretic security at the cost of higher latency. We validate these findings through application scenarios and network simulations, offering a roadmap for privacy-preserving sixth-generation vehicular architecture.

URI

https://hdl.handle.net/11511/120946

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Graduate School of Applied Mathematics, Conference / Seminar