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There is an urgent demand for privacy-preserving techniques capable ofsupporting compute and data intensive (CDI) computing in the era of big data.However, none of existing TEEs can truly support CDI computing tasks, as CDIrequires high throughput accelerators like GPU and TPU but TEEs do not offersecurity protection of such accelerators. This paper present HETEE(Heterogeneous TEE), the first design of TEE capable of strongly protectingheterogeneous computing with unsecure accelerators. HETEE is uniquelyconstructed to work with today's servers, and does not require any changes forexisting commercial CPUs or accelerators. The key idea of our design runssecurity controller as a stand-alone computing system to dynamically adjust theboundary of between secure and insecure worlds through the PCIe switches,rendering the control of an accelerator to the host OS when it is not neededfor secure computing, and shifting it back when it is. The controller is theonly trust unit in the system and it runs the custom OS and acceleratorruntimes, together with the encryption, authentication and remote attestationcomponents. The host server and other computing systems communicate withcontroller through an in memory task queue that accommodates the computingtasks offloaded to HETEE, in the form of encrypted and signed code and data.Also, HETEE offers a generic and efficient programming model to the host CPU.We have implemented the HETEE design on a hardware prototype system, andevaluated it with large-scale Neural Networks inference and training tasks. Ourevaluations show that HETEE can easily support such secure computing tasks andonly incurs a 12.34% throughput overhead for inference and 9.87% overhead fortraining on average.

Enabling Privacy-Preserving, Compute- and Data-Intensive Computing using Heterogeneous Trusted Execution Environment