Contextualizing Security and Privacy of Software-Defined Vehicles: A Literature Review and Industry Perspectives
It addresses security and privacy challenges for the automotive industry and Intelligent Transportation Systems, but it is incremental as it synthesizes existing knowledge without introducing new methods or data.
This work analyzes security and privacy in Software-Defined Vehicles (SDV) through a literature review and industry questionnaire, resulting in a security framework that emphasizes addressing architectural challenges, deploying layered security, and integrating privacy-preserving techniques to strengthen cybersecurity and V2X resilience.
The growing reliance on software in road vehicles has led to the emergence of Software-Defined Vehicles (SDV). This work analyzes SDV security and privacy through a systematic literature review complemented by an industry questionnaire across the automotive supply chain. The analysis is structured as four research questions and results in a security framework serving as a roadmap for SDV protection. The findings emphasize addressing mixed-criticality architectural challenges, deploying layered security mechanisms, and integrating privacy-preserving techniques. The results highlight the need to harmonize in-vehicle and cloud-based defenses to strengthen cybersecurity and V2X resilience in Intelligent Transportation Systems (ITS).