Achieving Data Dissemination with Security using FIWARE and Intel Software Guard Extensions (SGX)

TL;DR

This paper proposes a secure data dissemination solution for IoT applications using FIWARE and Intel SGX, ensuring privacy and integrity in public cloud environments without compromising system scalability or availability.

Contribution

It introduces a novel key management component integrating FIWARE with Intel SGX to enhance data security in IoT systems deployed on public clouds.

Findings

The solution maintains system scalability and availability.

It provides effective privacy, confidentiality, and integrity guarantees.

Memory costs are acceptable given the security benefits.

Abstract

The Internet of Things (IoT) field has gained much attention from industry and academia, being the main subject for numerous research and development projects. Frequently, the dense amount of generated data from IoT applications is sent to a cloud service, that is responsible for processing and storage. Many of these applications demand security and privacy for their data because of their sensitive nature. This is specially true when such data must be processed in entities hosted in public clouds, where the environment in which applications run may not be trusted. Some concerns are then raised since it is not trivial to provide the needed protection for these sensitive data. We present a solution that considers the security components of FIWARE and the Intel SGX capabilities. FIWARE is a platform created to support the development of Smart Applications, including IoT systems, and SGX is the Intel solution for Trusted Execution Environment (TEE). We propose a new component for key management that, together with other FIWARE components, can be used to provide privacy, confidentiality, and integrity guarantees for IoT data. A case study illustrates how this proposed solution can be employed in a realistic scenario, which allows the dissemination of sensitive data through public clouds without risking privacy issues. The results of the experiments provide evidence that our approach does not harm scalability or availability of the system. In addition, it presents acceptable memory costs when considering the benefit of the privacy guarantees achieved.