Blockchain-enabled trust management for secure content caching in mobile edge computing using deep reinforcement learning

Abstract

Mobile edge computing (MEC) has introduced content edge caching to offload network backhaul and improve user quality of experience by transferring frequently requested content to edge nodes near end-users. However, the implementation of edge caching is fraught with significant security and confidentiality issues due to the lack of trust between the different entities involved in the caching process. Most existing solutions for managing trust in edge caching only consider the trust between caching edge servers and ignore the trust between edge servers and content providers. This paper aims to fill this gap by proposing a trust management-based edge caching system that exploits the potential of blockchain technology to secure the caching process by enabling trust between content providers and edge servers. The proposed trust management system establishes trust in both directions of the interaction, providing higher confidence between the caching actors. Our trust system is based on a direct trust score calculated using the subjective three value logic scheme (3VSL). This is complemented by a reputation-based indirect trust score managed by the blockchain. A deep reinforcement learning (DRL) algorithm is proposed to efficiently make secure and intelligent caching decisions. The simulation results show that the proposed blockchain-based trust management system is effective and guarantees a good level of security and confidentiality for edge caching. They also demonstrate that our DRL caching algorithm provides secure and intelligent caching decisions in MEC, outperforming the baseline edge caching schemes.