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

There is an urgent demand for privacy-preserving techniques capable of supporting compute and data intensive (CDI) computing in the era of big data. However, none of existing TEEs can truly support CDI computing tasks, as CDI requires high throughput accelerators like GPU and TPU but TEEs do not offer security protection of such accelerators. This paper present HETEE (Heterogeneous TEE), the first design of TEE capable of strongly protecting heterogeneous computing with unsecure accelerators. HETEE is uniquely constructed to work with today's servers, and does not require any changes for existing commercial CPUs or accelerators. The key idea of our design runs security controller as a stand-alone computing system to dynamically adjust the boundary of between secure and insecure worlds through the PCIe switches, rendering the control of an accelerator to the host OS when it is not needed for secure computing, and shifting it back when it is. The controller is the only trust unit in the system and it runs the custom OS and accelerator runtimes, together with the encryption, authentication and remote attestation components. The host server and other computing systems communicate with controller through an in memory task queue that accommodates the computing tasks 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, and evaluated it with large-scale Neural Networks inference and training tasks. Our evaluations show that HETEE can easily support such secure computing tasks and only incurs a 12.34% throughput overhead for inference and 9.87% overhead for training on average.Comment: 16 pages, 7 figure

SESAME: Software defined Enclaves to Secure Inference Accelerators with Multi-tenant Execution

Hardware-enclaves that target complex CPU designs compromise both security and performance. Programs have little control over micro-architecture, which leads to side-channel leaks, and then have to be transformed to have worst-case control- and data-flow behaviors and thus incur considerable slowdown. We propose to address these security and performance problems by bringing enclaves into the realm of accelerator-rich architectures. The key idea is to construct software-defined enclaves (SDEs) where the protections and slowdown are tied to an application-defined threat model and tuned by a compiler for the accelerator's specific domain. This vertically integrated approach requires new hardware data-structures to partition, clear, and shape the utilization of hardware resources; and a compiler that instantiates and schedules these data-structures to create multi-tenant enclaves on accelerators. We demonstrate our ideas with a comprehensive prototype -- Sesame -- that includes modifications to compiler, ISA, and microarchitecture to a decoupled access execute (DAE) accelerator framework for deep learning models. Our security evaluation shows that classifiers that could distinguish different layers in VGG, ResNet, and AlexNet, fail to do so when run using Sesame. Our synthesizable hardware prototype (on a Xilinx Pynq board) demonstrates how the compiler and micro-architecture enables threat-model-specific trade-offs in code size increase ranging from 3-7 $\%$ and run-time performance overhead for specific defenses ranging from 3.96$\%$ to 34.87$\%$ (across confidential inputs and models and single vs. multi-tenant systems)