A Survey of Secure Computation Using Trusted Execution Environments
It provides a guideline for selecting TEE-based secure computation protocols for deployment, addressing security and performance needs in domains like privacy-preserving machine learning, but it is incremental as a survey.
This survey tackles the problem of systematically reviewing and comparing secure computation protocols using trusted execution environments (TEEs), resulting in a taxonomy and comprehensive assessment criteria for evaluating state-of-the-art methods.
As an essential technology underpinning trusted computing, the trusted execution environment (TEE) allows one to launch computation tasks on both on- and off-premises data while assuring confidentiality and integrity. This article provides a systematic review and comparison of TEE-based secure computation protocols. We first propose a taxonomy that classifies secure computation protocols into three major categories, namely secure outsourced computation, secure distributed computation and secure multi-party computation. To enable a fair comparison of these protocols, we also present comprehensive assessment criteria with respect to four aspects: setting, methodology, security and performance. Based on these criteria, we review, discuss and compare the state-of-the-art TEE-based secure computation protocols for both general-purpose computation functions and special-purpose ones, such as privacy-preserving machine learning and encrypted database queries. To the best of our knowledge, this article is the first survey to review TEE-based secure computation protocols and the comprehensive comparison can serve as a guideline for selecting suitable protocols for deployment in practice. Finally, we also discuss several future research directions and challenges.