A Hybrid AI-DEMATEL Model for Causal Analysis of Blockchain-Enabled Secure Communication and Privacy Risks in Access Control of Connected Autonomous Vehicles

Imagine a future where your car drives itself, navigating traffic, avoiding accidents, and communicating with other vehicles and city infrastructure without a human hand on the wheel. These machines, known as connected autonomous vehicles, rely on a constant, high-speed stream of data to make split-second decisions. To keep this system safe, the data must be protected from hackers, yet it also needs to be shared freely to ensure the car knows where it is going. This creates a delicate balancing act: how do you lock down a system to keep it secure without locking out the very information the car needs to function?

For years, experts have looked to a technology called blockchain to solve this problem. Think of blockchain as a shared digital ledger that records every transaction or message in a way that is nearly impossible to alter or fake. By using this system, vehicle manufacturers and governments hope to create a secure network where cars can verify each other's identity and share critical safety data without a central authority controlling the flow. However, simply plugging this technology into a car introduces a new set of complex problems. The very features that make blockchain secure can also make it slow, expensive to run, and difficult to manage when millions of cars are trying to talk at once.

A team of researchers from India has taken a deep dive into these specific challenges. They set out to understand the hidden connections between the various risks involved in using blockchain for car security. Instead of looking at these issues in isolation, they treated them as a web of interdependent factors. They wanted to know which problems are the root causes that trigger other issues, and which are merely the symptoms that appear when the system is under stress. To do this, they gathered insights from a panel of experts, including cybersecurity engineers, university researchers, and data scientists, and used a sophisticated analytical method to map out how these ten critical factors influence one another.

The researchers identified ten key areas that determine the success or failure of this technology. These range from "policy conflicts," where different authorities like police, car manufacturers, and owners might issue contradictory rules, to "latency," which is the tiny delay that occurs when the system verifies a message. Other factors include the sheer volume of data the network must handle, the limited computing power inside the car itself, and the difficulty of updating security rules while the car is moving. The team also examined the tension between keeping a driver's location private and the need to verify their identity for safety, as well as the risk of errors in the computer code that governs these rules.

By analyzing how these factors interact, the study revealed a clear hierarchy of cause and effect. The most critical finding was that "Interoperability" is the strongest causal factor, meaning it is the root cause that triggers the most downstream issues in the system. This refers to the technical inconsistencies between different blockchain platforms and vehicle manufacturers that underlie many downstream vulnerabilities. However, the study also highlighted that "Policy Conflicts" represents the most prominent and structurally complex factor overall. This refers to situations where legal and jurisdictional disagreements between stakeholders—such as governments, manufacturers, and vehicle owners—directly complicate real-time operations and decision-making, giving it the highest overall importance value in the system.

Following this, the study pointed to "trust and reputation" as another fundamental driver. In this digital ecosystem, a car's ability to participate depends on a score that reflects its past behavior. If a car has a history of sending false data, it is restricted. The researchers found that if this trust system is not robust, the entire network becomes unreliable. Similarly, the physical limitations of the car's onboard computer, known as resource constraints, act as a primary cause for delays. Because these computers have limited power, they struggle to process the heavy security checks required by blockchain, which in turn slows down the car's ability to react to emergencies.

The study also highlighted that "dynamic policy updates"—the ability to change security rules instantly during an emergency—are the most sensitive part of the system. These updates are not the cause of the problems, but rather the result of them. If the root causes like interoperability and resource limits are not fixed, the system cannot update its rules quickly enough to handle a crisis. For instance, if a car needs to share its location with emergency services during an accident, but the network is clogged or the rules are conflicting, that critical information might not get through in time.

Perhaps the most significant insight from the research is that trying to fix the symptoms directly is often ineffective. The study suggests that focusing solely on reducing delays or improving privacy without addressing the underlying causes will not work. Instead, manufacturers and policymakers must prioritize solving the root issues: making sure different car systems can communicate seamlessly, ensuring the onboard computers are powerful enough to handle the load, and creating clear, unified rules for how data is shared. By fixing these foundational problems, the other issues, such as slow response times and privacy concerns, naturally begin to resolve themselves.

The researchers validated their findings by using artificial intelligence tools to double-check their calculations, ensuring that the results were not just a product of human opinion but were mathematically consistent. Their work provides a roadmap for the future, suggesting that the path to safe, secure self-driving cars lies not in building stronger locks, but in building a more compatible and efficient network. As the industry moves toward a future where millions of autonomous vehicles share the road, understanding these causal links will be essential for designing a system that is both secure and capable of keeping us safe.