Data Enclave: A Data-Centric Trusted Execution Environment
Yuanchao Xu, James Pangia, Chencheng Ye, Yan Solihin, Xipeng Shen
Abstract
Trusted Execution Environments (TEEs) protect sensitive applications in the cloud with the minimal trust in the cloud provider. Existing TEEs with integrity protection however lack support for data management primitives, causing data sharing between enclaves either insecure or cumbersome. This paper proposes a new data abstraction for TEEs, data enclave. As a data-centric abstraction, data enclave is decoupled from an enclave's existence, is equipped with flexible secure permission controls, and crytographically isolated. It eliminates the hurdles for enclaves to cooperate efficiently, and at the same time, enables dynamic shrinking of the height of integrity tree for performance. This paper presents this new abstraction, its properties, and the architecture support. Experiments on synthetic benchmarks and three real-world applications all show that data enclave can help improve the efficiency of enclaves and inter-enclave cooperations significantly while enhancing the security protection.
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