Data Security Through Zero-Knowledge Proof and Statistical Fingerprinting in Vehicle-to-Healthcare Everything (V2HX) Communications

• Published in: IEEE transactions on intelligent transportation systems Vol. 22; no. 6; pp. 3869 - 3879
• Main Authors: Chaudhry, Junaid Ahsenali, Saleem, Kashif, Alazab, Mamoun, Zeeshan, Hafiz Maher Ali, Al-Muhtadi, Jalal, Rodrigues, Joel J. P. C.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Authentication; Biomedical imaging; Cloud computing; critical infrastructure; Cybersecurity; Data privacy; Fingerprinting; fog computing; Health care; Industries; information systems; IP networks; Medical services; Microprocessors; Mobile ad hoc networks; Provisioning; Remote monitoring; Security
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2021.3066487

The security and privacy of healthcare enterprises (HEs) are crucial because they maintain sensitive information. Because of the unique functional requirement of omni-inclusiveness, HEs are expected to monitor patients, allowing for connectivity with vehicular ad hoc networks (VANETs). In the absence of literature on security provisioning frameworks that connect VANETs and HEs, this paper presents a smart zero-knowledge proof and statistical fingerprinting-based trusted secure communication framework for a fog computing environment. A zero-knowledge proof is used for vehicle authentication, and statistical fingerprinting is employed to secure communication between VANETs and HEs. Authenticity verification of the operations is performed at the on-board unit (OBU) fitted in the vehicle based on the service executions at the resident hardware platform. The processor clock cycles are acquired from the service executions in a complete sandboxed environment. The calculated cycles assist in developing the blueprint signature for the particular OBU of the vehicle. Hence, the fingerprint signature helps build trust and plays a key role in authenticating the vehicle's horizontal movement to everything or to different sections of the HEs. In an environment enabled for fog computing, our novel model can provide efficient remote monitoring.