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RT-Trust: Automated refactoring for different trusted execution environments under real-time constraints

机译:RT-Trust:在实时约束下为不同可信执行环境提供自动重构

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Real-time systems must meet strict timeliness requirements. These systems also often need to protect their critical program information (CPI) from adversarial interference and intellectual property theft. Trusted execution environments (TEE) execute CPI tasks on a special-purpose processor, thus providing hardware protection. However, adapting a system written to execute in environments without TEE requires partitioning the code into untrusted and trusted parts. This process involves complex manual program transformations that are not only laborious and intellectually tiresome, but also hard to validate and verify adherence to real-time constraints. To address these problems, this paper presents novel program analyses and transformation techniques, accessible to the developer via a declarative meta-programming model. The developer declaratively specifies the CPI portion of the system. A custom static analysis checks CPI specifications for validity, while probe-based profiling helps identify whether the transformed system would continue to meet the original real-time constraints, with a feedback loop suggesting how to modify the code, so its CPI can be isolated. Finally, an automated refactoring isolates the CPI portion for TEE-based execution, communicated with through generated calls to the TEE API. The reference implementation of our approach profiles and transforms real-time systems to isolate their CPI functions to execute on two different TEE platforms: OP-TEE and SGX. Although these platforms substantially differ in terms of their respective APIs and performance characteristics, our refactoring completely hides these differences from the developer by automatically synthesizing the correct CPI functionality required for these dissimilar TEE implementations. We have evaluated our approach by successfully enabling the trusted execution of the CPI portions of several microbenchmarks and a drone autopilot. Our approach shows the promise of declarative meta-programming in reducing the programmer effort required to adapt systems for trusted execution under real-time constraints.
机译:实时系统必须满足严格的及时性要求。这些系统通常还需要保护他们的关键计划信息(CPI)免受对抗的干扰和知识产权盗窃。可信执行环境(TEE)在专用处理器上执行CPI任务,从而提供硬件保护。但是,调整写入的系统在没有TEE的环境中执行的系统需要将代码划分为不受信任和可信的部分。此过程涉及复杂的手动程序变换,不仅是艰苦的和智力令人厌恶的,而且难以验证和验证依从对实时约束。为解决这些问题,本文介绍了新颖的程序分析和转换技术,可通过声明性元编程模型访问开发人员。开发人员声明地指定了系统的CPI部分。自定义静态分析检查CPI规范是否有效性,而基于探测的分析有助于确定转换后的系统是否会继续满足原始的实时约束,具有反馈循环,建议如何修改代码,因此可以隔离其CPI。最后,自动重构将CPI部分隔离为基于TEE的执行,通过生成的调用与TEE API通信。我们的方法配置文件的参考实现并将实时系统转换为隔离其CPI函数以在两个不同的TEE平台上执行:OP-TEE和SGX。尽管这些平台在各自的API和性能特征方面大大不同,但是我们的重构通过自动综合这些不同的三通实现所需的正确CPI功能来完全隐藏与开发人员的这些差异。我们通过成功地启用多个微波发出的CPI部分和无人机自动驾驶仪的CPI部分来评估我们的方法。我们的方法展示了声明性元编程的承诺,减少了在实时约束下调整可信执行的程序员所需的程序员努力。

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