Secure Data Provenance in the Internet of Vehicles

This PhD project will develop a novel secure data provenance scheme for the Internet of Vehicles (IoV) to ensure data security and originality, and improved confidence in decision-making. Secure data provenance in IoV will facilitate trust in the data migrated to different devices and systems and underpin the delivery of services.

In this project a new approach towards data provenance is targeted, optimised for the mobile and multi-hop connected vehicles environment and able to provide guarantees on source identity authenticity, source data authenticity, data integrity, location authenticity and data consistency.

The proposed data provenance framework will formally synthesise and articulate the defined properties in an IoV context and be formally verified for security correctness and evaluated for performance through simulations and testbed implementation.

Participants

• Deakin University

Project background

The widespread adoption of autonomous vehicles has led to the need for confidence in the originality and security of data i.e., data provenance. Data provenance in IoV has not been a strong focus of research to date with current solutions disparate and failing to address all required properties – source authenticity, data integrity, location authenticity.

In IoV, data provenance is needed to ensure that data that is used for decision-making is reliable and has not been tampered with. In addition, there needs to be guarantees on the source, originality and freshness of the received data. Hence, this is a non-trivial problem that needs to be addressed through a wholistic framework for data provenance that can balance security requirement with performance expectations.

The motivation for undertaking this research is discussed below:

• There is a lack of a comprehensive (wholistic) data Provenance Framework for IoV. This has provided an opportunity to explore the development of a novel data provenance framework for IoV.
• Current data provenance frameworks suffer from security issues and do not support mobility and multi-hop environments. Although some researchers have tried to address mobility characteristics of the IoV, security still remains a concern.
• There is a lack of a proper framework that supports location authenticity which is one of the important properties of data provenance in a mobile environment. Most of the researchers have ignored this important property of secure data provenance.
• There is lack of a framework that maintains source identity authenticity so that cloning and tampering attacks can be prevented. Although some researchers have proposed the use of techniques such as physically unclonable functions (PUFs), using PUFs may not be applicable in different conditions and the reliability of PUFs is an open question.

To address this gap in knowledge, in this research a new approach for data provenance in the IoV is proposed. The research aims to develop a framework that can achieve secure data provenance in mobile and multi-hop environments with properties such as source identity authenticity, source data authenticity, data integrity, location authenticity, and anomaly detection.

The proposed data provenance framework and methods will formally synthesise and articulate the defined properties on IoV. The proposed research aims to investigate the originality of the data across different devices and systems.

This research will develop a specific scheme to prevent the source device from tampering and cloning attacks. Similarly, it will also implement a technique to authenticate the source data preventing different types of attacks.

Furthermore, it will develop a technique to ensure the data integrity of the system, authenticating the data from the real source location. Finally, ensuring the anomalies are detected from the system’s perspective of the data consistency.

Project objectives

The main objective of this research is to design and develop a secure and efficient data provenance framework for end-to-end message validation in mobile, multi-hop IoV environments.

The following research questions will be pursued to achieve the above research objective:

1. How can secure data provenance be integrated into the end-to-end message validation model in IoV satisfying both mobility and multi-hop communication?
2. How can end-to-end message validation techniques be implemented to achieve secure data provenance in IoV with low authentication delay and communication overhead?
3. How can each of the properties of source identity authenticity, source data authenticity, data integrity, location authenticity, and anomaly detection be achieved together?
   – How can source device tampering and cloning attacks be prevented by applying source identity and source data authentication techniques to ensure originality and trust in the system?
   – How can the confidence of integrity of the sources be maintained including their location information?
4. How does the integration of secure data provenance impact the performance of IoV?
   – What will be the impact on the network overhead in IoV?
   – What will be the impact of the delays on decision-making in IoV?

The proposed data provenance framework will formally synthesise and articulate the defined properties in an IoV context and be formally verified for security correctness and evaluated for performance through simulations and testbed implementation.

Please note …

This page will be a living record of this project. As it matures, hits milestones, etc., we’ll continue to add information, links, images, interviews and more. Watch this space!