Privacy-Aware Machine Learning Algorithms for Autonomous Vehicle Data Analysis
Abstract
In the smart mobility domain, AI-enabled systems are designed to enable machines to make decisions autonomously without the intervention of a human operator. One of the two most important scenarios in which AI solves problems are: (a) in the Inter-Vehicular Communications (IVC) systems, smart vehicles are designed to react directly to the environment in which they operate and (b) in the In-Car systems, smart vehicles are less autonomous than the ones in category (a) but can still perform a wide spectrum of functions. In researches, it is identified that in scenario (a), privacy should be preferentially preserved over other concerns and in scenario (b), privacy is crucial but not is equivocally relevant as in scenario (a) [1]. The aim is to enable vehicles to work in competition and cooperation with humans according to privacy laws while trading off privacy with their performance in functions required such as achieving a compromise to maximize the efficiency of traffic flows.
[2] We are in the middle of a paradigm shift from traditional analytics to data-driven, predictive, and autonomous computing that can be seen in the massive deployment of systems based on Artificial Intelligence (AI) such as smart vehicles, machine learning-based services, and decision systems in the Industry 4.0 scenario. Such a change is an opportunity for human advancement because AI-enabled systems are expected to outperform humans in most tasks. However, the AI paradigm shift creates questions about the social impact, laws, accountability and responsibility of AI-enabled systems and necessitates the need for tools and methodologies to protect the ‘welldness’ of society. The host of adopted solutions to protect rights, safety, and balance in society ranges from the introduction of logical, ethical elements in programming and AI systems in a formalized way to the design of secure systems with strict boundaries that do not invade the values involved.
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