  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.
