Practical hybrid confidentiality-based analytics framework with Intel SGX

Abstract

Massive cloud infrastructure capabilities, including efficient, scalable, and elastic computing resources, have led to a widespread adoption of Internet of Things (IoT) cloud-enabled services. This involves giving complete control to cloud service providers (CSPs) of sensitive IoT data by moving data storage and processing in cloud. An efficient and lightweight advanced encryption standard (AES) cryptosystem can play a major role in protecting IoT data from exposure to CSPs by protecting the privacy of outsourced data. However, AES lacks computation capabilities, which is a critical factor that prevents individuals and organizations from taking full advantage of cloud computing services. When Intel software guard extensions (SGX) is used with AES cryptosystem, the developing framework can provide a practical solution to build a confidentiality-based data analytics framework for IoT-enabled applications in various domains. In this paper, a privacy-preserving data analytics framework is developed that relies on a hybrid-integrated approach, in which both software- and hardware-based solutions are applied to ensure confidentiality and process-sensitive outsourced data in the cloud environment.