PRIVACY DETECTION BY A COMPREHENSIVE REVIEW OF MACHINE LEARNING AND DEEP LEARNING TECHNIQUES TO ENHANCE SECURE DATA SHARING IN AUTONOMOUS VEHICLES

Abstract

Autonomous and connected vehicles (ACVs) are changing the transportation of the modern world they combine sophisticated sensing, communication, and modern technologies. These vehicles will keep on gathering, processing and distributing a significant amount of data about drivers, passengers and the environments around them. Although such data is necessary in terms of safety, navigation and intelligent decision-making, it also brings major privacy and security concerns. There is a chance that sensitive information can be revealed to cyberattacks, abuse, or unwarranted monitoring such as location history, driving behavior, and personal identifiers. The paper summarizes the key privacy and security issues in autonomous and connected vehicles, discusses current protection methods that include feder- ated learning, homomorphic encryption, and differential privacy, and also outlines the gaps in research. It tries to give a general overview of current issues and solutions to the problems at the undergraduate level and stresses the need of privacy-sensitive design in the next generation of intelligent transport systems.

Keywords : Connected Vehicles, Autonomous Vehicles, Data Privacy, Cybersecurity, Internet of Vehicles, Privacy-Preserving Machine Learning.