IoT devices provide a rich data source that is not available in the past, which is valuable for a wide range of intelligence applications, especially deep neural network (DNN) applications that are data-thirsty. An established DNN model in turn provides useful analysis results that can improve the operation of IoT systems. The progress in distributed/federated DNN training further unleashes the potential of integration of IoT and intelligence applications. When a large number of IoT devices deployed in different physical locations, distributed training allows training modules to be deployed to multiple edge data centers that are close to the IoT devices to reduce the latency and movement of large amounts of data. In practice, these IoT devices and edge data centers are usually owned and managed by different parties, who do not fully trust each other or have conflicting interests. It is hard to coordinate them to provide an end-to-end integrity protection of the DNN construction and application with classical security enhancement tools. For example, one party may share an incomplete data set with others, or contribute a modified sub DNN model to manipulate the aggregated model and affect the decision-making process. To mitigate this risk, we propose a novel blockchain based end-toend integrity protection scheme for DNN applications integrated with an IoT system in the edge computing environment. The protection system leverages a set of cryptography primitives to build a blockchain adapted for edge computing that is scalable to handle a large number of IoT devices. The customized blockchain is integrated with a distributed/federated DNN to offer integrity and authenticity protection services.
Xu, L., Gao, Z., Fan, X., Chen, L., Kim, H., Suh, T., & Shi, W. (2020). Blockchain based End-to-end Tracking System for Distributed IoT Intelligence Application Security Enhancement. 19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom 2020).
19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom 2020).