Hiding Decryption Latency in Intel SGX using Metadata Prediction

Hardware-Assisted Trusted Execution Environment technologies have become a crucial component in providing security for cloud-based computing. One of such hardware-assisted countermeasures is Intel Software Guard Extension (SGX). Using additional dedicated hardware and a new set of CPU instructions, SGX is able to provide isolated execution of code within trusted hardware containers called enclaves. By utilizing private encrypted memory and various integrity authentication mechanisms, it can provide confidentiality and integrity guarantees to protected data. In spite of dedicated hardware, these extra layers of security add a significant performance overhead. Decryption of data using secret OTPs, which are generated by modified Counter Mode Encryption AES blocks, results in a significant latency overhead that contributes to the overall SGX performance loss. This thesis introduces a metadata prediction extension to SGX based on local metadata releveling and prediction mechanisms. Correct prediction of metadata allows to speculatively precompute OTPs, which can be immediately used in decryption of incoming ciphertext data. This hides a significant part of decryption latency and results in faster SGX performance without any changes to the original SGX security guarantees.