Abstract

By successfully implementing data exchange, phone computation (MEC) plays a vital role in allowing a variety of service applications. However, the distinctive features of MEC also cause issues with privacy and data security which hinders the growth of MEC. A potential solution to ensure the security and authenticity of data exchange is blockchain. However, due to the changing nature of channel capacity and limited bandwidth, integrating cryptocurrency into the MEC system is a difficult task. In this paper, we use an asymmetric learning technique to propose a highly secure exchange mechanism for the bitcoin MEC system. First, an architecture for safe data exchange in the MEC system that is enabled by blockchain is provided. Then, based on the system resources on hand and the users' expectations for privacy, we provide a customizable secrecy technique. In order to enhance system performance while minimising MEC system energy consumption and increasing blockchain network throughput, a safe data sharing optimization problem is then developed in the blockchain-enabled MEC system. In particular, an asynchronous learning strategy is used to address the posed issue. Comparing our suggested safe data sharing technique to various well-known reference techniques in terms of typical performance, energy consumption, and reward, the numerical results indicate that it is better.

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