Integration of blockchain with connected and autonomous vehicles : vision and challenge

Dargahi, T; Ahmadvand, H; Alraja, MN; Yu, C-M

Abstract

Connected and Autonomous Vehicles (CAVs) are introduced to improve individuals’ quality of life by offering a wide range of services.
They collect a huge amount of data and exchange them with each other and the infrastructure. The collected data usually includes
sensitive information about the users and the surrounding environment. Therefore, data security and privacy are among the main
challenges in this industry. Blockchain, an emerging distributed ledger, has been considered by the research community as a potential
solution for enhancing data security, integrity and transparency in Intelligent Transportation Systems (ITS). However, despite the
emphasis of governments on the transparency of personal data protection practices, CAV stakeholders have not been successful in
communicating appropriate information with the end-users regarding the procedure of collecting, storing and processing their personal
data, as well as the data ownership. This paper provides a vision of the opportunities and challenges of adopting blockchain in ITS from
the “data transparency" and “privacy" perspective. The main aim is to answer the following questions: (1) Considering the amount of
personal data collected by the CAVs, such as location, how the integration of blockchain technology would affect transparency, fairness
and lawfulness of personal data processing concerning the data subjects (as this is one of the main principles in the existing data
protection regulations)? (2) How the trade-off between transparency and privacy can be addressed in blockchain-based ITS use cases?

Citation

Dargahi, T., Ahmadvand, H., Alraja, M., & Yu, C. (2022). Integration of blockchain with connected and autonomous vehicles : vision and challenge. Journal of Data and Information Quality, 14(1), 5. https://doi.org/10.1145/3460003

• Journal Article Type: Article
• Acceptance Date: Apr 3, 2021
• Online Publication Date: Dec 11, 2021
• Publication Date: Mar 1, 2022
• Deposit Date: Aug 11, 2021
• Publicly Available Date: Dec 20, 2021
• Journal: Journal of Data and Information Quality
• Print ISSN: 1936-1955
• Electronic ISSN: 1936-1963
• Publisher: Association for Computing Machinery (ACM)
• Volume: 14
• Issue: 1
• Pages: 5
• DOI: https://doi.org/10.1145/3460003
• Publisher URL: https://doi.org/10.1145/3460003
• Related Public URLs: https://dl.acm.org/journal/jdiq
• Additional Information: Access Information : © ACM (2021). This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Journal of Data and Information Quality, http://dx.doi.org/10.1145/3460003; Funders : UK Royal Society;Research Council (TRC), Sultanate of Oman;Ministry of Science and Technology (MOST), Taiwan; Grant Number: IEC\R3\183047; Grant Number: BFP/RGP/ICT/19/186; Grant Number: MOST 110-2636-E-009-018 and 110-2927-I-009-510