Framework Design for Privacy Preserving Machine Learning in a 6G Vehicular Network
19th International Conference on Computational Intelligence in Security for Information Systems-CISIS, Marbella, İspanya, 18 - 19 Haziran 2026, cilt.3013, ss.203-211, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Cilt numarası: 3013
- Doi Numarası: 10.1007/978-3-032-29251-3_17
- Basıldığı Şehir: Marbella
- Basıldığı Ülke: İspanya
- Sayfa Sayıları: ss.203-211
- Orta Doğu Teknik Üniversitesi Adresli: Evet
Özet
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.