Biblio

The massive boom of Internet of Things (IoT) has led to the explosion of smart IoT devices and the emergence of various applications such as smart cities, smart grids, smart mining, connected health, and more. While the proliferation of IoT systems promises many benefits for different sectors, it also exposes a large attack surface, raising an imperative need to put security in the first place. It is impractical to heavily rely on manual operations to deal with security of massive IoT devices and applications. Hence, there is a strong need for securing IoT systems with minimum human intervention. In light of this situation, in this paper, we envision security automation and orchestration for IoT systems. After conducting a comprehensive evaluation of the literature and having conversations with industry partners, we envision a framework integrating key elements towards this goal. For each element, we investigate the existing landscapes, discuss the current challenges, and identify future directions. We hope that this paper will bring the attention of the academic and industrial community towards solving challenges related to security automation and orchestration for IoT systems.

Crowdsensing, driven by the proliferation of sensor-rich mobile devices, has emerged as a promising data sensing and aggregation paradigm. Despite useful, traditional crowdsensing systems typically rely on a centralized third-party platform for data collection and processing, which leads to concerns like single point of failure and lack of operation transparency. Such centralization hinders the wide adoption of crowdsensing by wary participants. We therefore explore an alternative design space of building crowdsensing systems atop the emerging decentralized blockchain technology. While enjoying the benefits brought by the public blockchain, we endeavor to achieve a consolidated set of desirable security properties with a proper choreography of latest techniques and our customized designs. We allow data providers to safely contribute data to the transparent blockchain with the confidentiality guarantee on individual data and differential privacy on the aggregation result. Meanwhile, we ensure the service correctness of data aggregation and sanitization by delicately employing hardware-assisted transparent enclave. Furthermore, we maintain the robustness of our system against faulty data providers that submit invalid data, with a customized zero-knowledge range proof scheme. The experiment results demonstrate the high efficiency of our designs on both mobile client and SGX-enabled server, as well as reasonable on-chain monetary cost of running our task contract on Ethereum.