A Secure Data Aggregation Strategy in Edge Computing and Blockchain-Empowered Internet of Things

With the rapid development of the Internet of Things (IoT), more and more data are generated by smart devices to support various edge services. Since these data may contain sensitive information, security and privacy of data aggregation has become a key challenge in IoT. To tackle this problem, a Blockchain based Secure Data Aggregation strategy, namely (BSDA), is proposed for edge computing empowered IoT. Specifically, in order to restrict task receivers (i.e., Mobile Data Collectors (MDC)) to search and accept tasks, the block header is intergraded with a security label including task security level and task completion requirement. Accordingly, new block generation rules are developed to improve system performance in throughput and transaction latency. Furthermore, BSDA decomposes both sensitive tasks and task receivers into groups against privacy disclosure. On the other hand, a deep reinforcement learning method, the Improved double bootstrapped Deep Deterministic Policy Gradient (IDDPG), is developed to design energy efficient MDC routes under the constrains that the security levels of MDCs should be higher than the security levels of data aggregation tasks. Simulation results indicate that (i) as a privacy-preserving strategy, BSDA obtains high throughput and low transaction latency; (ii) BSDA outperforms certain contemporary strategies in aggregation ratio and energy cost.