Big Data and Privacy Issues for Connected Vehicles in Intelligent Transportation Systems

The evolution of Big Data in large-scale Internet-of-Vehicles has brought forward unprecedented opportunities for a unified management of the transportation sector, and for devising smart Intelligent Transportation Systems. Nevertheless, such form of frequent heterogeneous data collection between the vehicles and numerous applications platforms via diverse radio access technologies has led to a number of security and privacy attacks, and accordingly demands for a secure data collection in such architectures. In this respect, this chapter is primarily an effort to highlight the said challenge to the readers, and to subsequently propose some security requirements and a basic system model for secure Big Data collection in Internet-of-Vehicles. Open research challenges and future directions have also been deliberated.

💡 Research Summary

The chapter provides a comprehensive examination of the privacy and security challenges that arise when massive, heterogeneous data are collected and processed in Internet‑of‑Vehicles (IoV) environments supporting Intelligent Transportation Systems (ITS). It begins by describing the architecture of modern connected‑vehicle ecosystems, where vehicles, roadside units, edge servers, and cloud platforms exchange high‑frequency sensor streams (position, speed, environmental conditions, driver behavior) via multiple radio access technologies such as 5G, C‑V2X, and DSRC. This data‑rich environment enables advanced traffic management, predictive maintenance, and autonomous driving services, but also creates a large attack surface.

The authors identify three principal threat domains. First, during data acquisition, raw vehicle telemetry often contains personally identifiable information (PII). Uncontrolled collection can enable tracking, profiling, and location‑based attacks. Second, the transmission phase is vulnerable to man‑in‑the‑middle, replay, and tampering attacks because data traverse heterogeneous networks with dynamic routing and multiple intermediate nodes that may lack mutual trust. Third, in storage and analytics, centralized big‑data repositories and cloud‑based machine‑learning pipelines are attractive targets for insider threats, database breaches, and unauthorized data mining. Moreover, models trained on raw vehicle data can inadvertently memorize sensitive patterns, exposing privacy through model inversion attacks.

To mitigate these risks, the chapter proposes five core security requirements. (1) Robust authentication and authorization using a PKI framework and dynamic access‑control policies to ensure that only verified entities can inject or retrieve data. (2) Data integrity and provenance verification through distributed ledger or blockchain mechanisms that immutably record hash‑based fingerprints of each data packet, enabling tamper detection and auditability. (3) Privacy‑preserving analytics employing differential privacy, homomorphic encryption, and federated learning so that raw data never leave the vehicle or edge node in clear form. (4) Real‑time intrusion detection by deploying AI‑driven anomaly detectors at the edge, capable of flagging abnormal traffic patterns or credential misuse with low latency. (5) Regulatory and standards alignment with GDPR, ISO/SAE 21434, NIST Cybersecurity Framework, and emerging automotive‑specific guidelines to ensure legal compliance and interoperability.

A layered system model is introduced to operationalize these requirements. At the vehicle layer, a lightweight security agent encrypts sensor outputs, attaches signed tokens, and performs local anomaly checks. The radio access layer (RAN) validates tokens, performs traffic shaping, and logs metadata to a local ledger. The edge layer aggregates data, executes privacy‑preserving transformations, records immutable hashes on a blockchain, and participates in federated model updates. Finally, the cloud layer conducts large‑scale analytics and service provisioning, but all queries are subject to strict least‑privilege controls and continuous audit logging.

The authors acknowledge several open research challenges. Scaling PKI to millions of moving vehicles raises certificate distribution and revocation overhead. Blockchain consensus mechanisms introduce latency and energy consumption that may conflict with real‑time ITS requirements. Federated learning suffers from communication overhead and convergence issues in highly dynamic vehicular topologies. Consequently, future work should focus on lightweight cryptographic protocols, quantum‑resistant algorithms, standardized APIs for cross‑domain security orchestration, and policy‑driven automation. Moreover, extensive simulation and field‑trial deployments across urban, highway, and autonomous‑driving scenarios are needed to validate the proposed framework’s effectiveness and performance.

In summary, while big‑data‑driven IoV promises unprecedented improvements in traffic efficiency, safety, and user experience, these benefits can only be realized if privacy and security are embedded into every layer of the system. This chapter delineates the threat landscape, articulates concrete security requirements, and offers a practical architectural blueprint, thereby providing researchers and practitioners with a solid foundation for building trustworthy, privacy‑preserving intelligent transportation infrastructures.