# Secure Data Offloading Strategy for Connected and Autonomous Vehicles

## TL;DR

This paper introduces a secure data offloading method for connected vehicles using Random Linear Network Coding to minimize eavesdropping risks in fog-based intelligent transportation systems.

## Contribution

It proposes a novel RLNC-based data offloading strategy to enhance security in CAV communications within fog computing infrastructure.

## Key findings

- Effective in reducing eavesdropper data recovery probability
- Suitable for large-scale ITS networks
- Preliminary results show promising security improvements

## Abstract

Connected and Automated Vehicles (CAVs) are expected to constantly interact with a network of processing nodes installed in secure cabinets located at the side of the road -- thus, forming Fog Computing-based infrastructure for Intelligent Transportation Systems (ITSs). Future city-scale ITS services will heavily rely upon the sensor data regularly off-loaded by each CAV on the Fog Computing network. Due to the broadcast nature of the medium, CAVs' communications can be vulnerable to eavesdropping. This paper proposes a novel data offloading approach where the Random Linear Network Coding (RLNC) principle is used to ensure the probability of an eavesdropper to recover relevant portions of sensor data is minimized. Our preliminary results confirm the effectiveness of our approach when operated in a large-scale ITS networks.