Quantum-Resistant Privacy Preservation for Mobile Healthcare Services in Connected Transportation Systems via Deep Neural Architectures

The rapid convergence of connected transportation networks and real-time healthcare services has given rise to new security and privacy challenges. Conventional cryptographic mechanisms, primarily designed for classical adversaries, may soon be rendered obsolete by quantum computers, posing dire risks to the confidentiality of sensitive medical data. This work proposes a quantum-resistant privacy preservation framework for mobile healthcare systems operating in vehicular networks. Leveraging lattice-based cryptography-specifically Ring Learning-with-Errors (Ring-LWE)-our approach ensures robust encryption and key management, rendering patient data impervious to quantum-based attacks. Complementing this cryptographic layer is a deep neural network architecture that integrates convolutional and attention-based modules to detect network anomalies with high accuracy and minimal latency. We demonstrate the feasibility of our method through comprehensive experiments that measure (1) cryptographic overhead, (2) intrusion detection effectiveness, and (3) end-to-end system performance under realistic conditions and varied load scenarios. Experimental results show that the proposed scheme can maintain sub-100 ms end-to-end latencies for healthcare data transfer in high-traffic urban networks, detecting a wide range of attacks at accuracy levels exceeding 95%. These findings underscore the potential of combining post-quantum cryptographic primitives with advanced deep learning to secure time-sensitive medical applications within next-generation intelligent transportation systems.