A Secure Remote Monitoring Framework Supporting Efficient Fine-Grained Access Control and Data Processing in IoT

Abstract

As an important application of the Internet-of-Things, many remote monitoring systems adopt a device-to-cloud network paradigm. In a remote patient monitoring (RPM) case, various resource-constrained devices are used to measure the health conditions of a target patient in a distant non-clinical environment and the collected data are sent to the cloud backend of an authorized health care provider (HCP) for processing and decision making. As the measurements involve private patient information, access control, confidentiality, and trustworthy processing of the data become very important. Software-based solutions that adopt advanced cryptographic tools, such as attribute-based encryption and fully homomorphic encryption, can address the problem, but they also impose substantial computation overhead on both patient and HCP sides. In this work, we deviate from the conventional software-based solutions and propose a secure and efficient remote monitoring framework using latest hardware-based trustworthy computing technology, such as Intel SGX. In addition, we present a robust and lightweight “heartbeat” protocol to handle notoriously difficulty user revocation problem. We implement a prototype of the framework for PRM and show that the proposed framework can protect user data privacy against unauthorized parties, with minimum performance cost compared to existing software-based solutions with such strong privacy protection.