A unified privacy preserving model with AI at the edge for Human-in-the-Loop Cyber-Physical Systems

Abstract

With the proliferation of personal Internet of Things (IoT) devices and their wide adoption among society, the original notion of IoT has been reshaped, giving rise to new IoT-based paradigms like Human-in-the-Loop Cyber-Physical Systems (HiTLCPS). While these systems can bring benefits and positively influence people, their pervasive nature raises significant privacy concerns, especially regarding the acquisition and processing of data. Although privacy-preserving mechanisms have been proposed for these implementations, the existing approaches tend to focus on either data acquisition or data processing. However, to date, no solution has encompassed the entire process. In this regard, this paper presents a unified privacy-preserving model for HiTLCPS. This approach integrates a human-centric mechanism to control and make data acquisition and sharing tasks transparent with a state inference process supported by Artificial Intelligence (AI) in the edge. A set of assessments were conducted in actual implementation to evaluate the feasibility of the model. Our findings reveal that our federated learning approach is a suitable solution compared to the traditional approach based on machine learning at the small cost of 3.27% of average accuracy. Finally, this paper concludes by providing a roadmap towards integrating HiTLCPS from diverse contexts, including a state inference process closer to the user domain.