Building Enclave-Native Storage Engines for Practical Encrypted Databases

Data confidentiality is one of the biggest concerns that hinders enterprise customers from moving their workloads to the cloud. Thanks to the trusted execution environment (TEE), it is now feasible to build encrypted databases in the enclave that can process customers' data while keeping it confidential to the cloud. Though some enclave-based encrypted databases emerge recently, there remains a large unexplored area in between about how confidentiality can be achieved in different ways and what influences are implied by them. In this paper, we first provide a broad exploration of possible design choices in building encrypted database storage engines, rendering trade-offs in security, performance and functionality. We observe that choices on different dimensions can be independent and their combination determines the overall trade-off of the entire storage. We then propose Enclage , an encrypted storage engine that makes practical trade-offs. It adopts many enclave-native designs, such as page-level encryption, reduced enclave interaction, and hierarchical memory buffer, which offer high-level security guarantee and high performance at the same time. To make better use of the limited enclave memory, we derive the optimal page size in enclave and adopt delta decryption to access large data pages with low cost. Our experiments show that Enclage outperforms the baseline, a common storage design in many encrypted databases, by over 13x in throughput and about 5x in storage savings.