Context-aware privacy preservation in a hierarchical fog computing system

Fog computing faces various security and privacy threats. Internet of Things (IoTs) devices have limited computing, storage, and other resources. They are vulnerable to attack by adversaries. Although the existing privacy-preserving solutions in fog computing can be migrated to address some privacy issues, specific privacy challenges still exist because of the unique features of fog computing, such as the decentralized and hierarchical infrastructure, mobility, location and content-aware applications. Unfortunately, privacy-preserving issues and resources in fog computing have not been systematically identified, especially the privacy preservation in multiple fog node communication with end users. In this paper, we propose a dynamic MDP-based privacy-preserving model in zero-sum game to identify the efficiency of the privacy loss and payoff changes to preserve sensitive content in a fog computing environment. First, we develop a new dynamic model with MDP-based comprehensive algorithms. Then, extensive experimental results identify the significance of the proposed model compared with others in more effectively and feasibly solving the discussed issues.