Mobile-edge computing (MEC) plays a significant role in enabling diverse service applications by implementing efficient data sharing. However, the unique characteristics of MEC also bring data privacy and security problem, which impedes the development of MEC. Blockchain is viewed as a promising technology to guarantee the security and traceability of data sharing. Nonetheless, how to integrate blockchain into MEC system is quite challenging because of dynamic characteristics of channel conditions and network loads. To this end, we propose a secure data sharing scheme in the blockchain-enabled MEC system using an asynchronous learning approach in this article. First, a blockchain-enabled secure data sharing framework in the MEC system is presented. Then, we present an adaptive privacy-preserving mechanism according to available system resources and privacy demands of users. Next, an optimization problem of secure data sharing is formulated in the blockchain-enabled MEC system with the aim to maximize the system performance with respect to the decreased energy consumption of MEC system and the increased throughput of blockchain system. Especially, an asynchronous learning approach is employed to solve the formulated problem. The numerical results demonstrate the superiority of our proposed secure data sharing scheme when compared with some popular benchmark algorithms in terms of average throughput, average energy consumption, and reward.

中文翻译：

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移动边缘计算中支持区块链的安全数据共享方案：一种异步优势 Actor__ritic 学习方法


移动边缘计算（MEC）通过实现高效的数据共享，在实现多样化服务应用方面发挥着重要作用。然而MEC独特的特性也带来了数据隐私和安全问题，阻碍了MEC的发展。区块链被视为一种有前途的技术，可以保证数据共享的安全性和可追溯性。尽管如此，由于信道条件和网络负载的动态特性，如何将区块链集成到MEC系统中是相当具有挑战性的。为此，我们在本文中使用异步学习方法在支持区块链的 MEC 系统中提出了一种安全数据共享方案。首先，提出了MEC系统中基于区块链的安全数据共享框架。然后，我们根据可用的系统资源和用户的隐私需求提出了一种自适应隐私保护机制。接下来，在支持区块链的 MEC 系统中提出安全数据共享的优化问题，目的是在 MEC 系统能耗降低和区块链系统吞吐量增加方面最大化系统性能。特别是，采用异步学习方法来解决所提出的问题。数值结果证明了我们提出的安全数据共享方案与一些流行的基准算法在平均吞吐量、平均能耗和奖励方面的优越性。