Secure and privacy-preserving fog-assisted vehicular crowdsensing
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Vehicular crowdsensing (VCS) is an emerging paradigm where vehicles use onboard sensors to collect and share data with the aim of measuring phenomena of common interest. VCS has allowed for automated control as well as analysis of road surface quality. These innovations have thus encouraged and shown the importance of the cloud in providing reliable transport services to clients. Nonetheless, these initiatives have not been without challenges, ranging from mobility support, location awareness, low latency and geo-distribution. In order to address these challenges, a new term, known as fog computing, has been coined as a novel paradigm. Therefore, this present work exploits the advantages of VCS and fog computing paradigms in order to propose a promising framework, which is referred to as fog-assisted vehicular crowdsensing (FVCS). Although FVCS has addressed the aforementioned challenges, it may encounter various security threats and privacy concerns that could jeopardize public safety and become the main barrier to the acceptance of such a new technology. This thesis presents the proposal of a secure and privacy-preserving framework for FVCS. Its objective is to allow vehicles to share their resources while preserving their privacy by preventing private information from being disclosed. Attention is first focused on investigating the threat towards the data generated by vehicles, which is then forwarded to cloud servers and organizations by roadside units (RSUs). Therefore, this work presents a privacy-preserving protocol for enhancing security in VCS-based road surface condition monitoring systems using fog computing. Furthermore, in order to revoke compromised users from the system, this work offers a novel secure and efficient revocable privacy-preserving protocol in FVCS. Based on the CLASC scheme, this protocol is designed with security properties that include report confidentiality, integrity, privacy, revocation functionality and key escrow resilience. In addition to the above countermeasures in FVCS, this work also presents an efficient deduplicated reporting scheme in order to ensure that vehicles are free from security risks and privacy threats while sharing their resources with semi-trusted nodes. Moreover, attention is also given to how the present work can be further developed by exploring the important business perspective related to FVCS technology.