Generalized external interaction with tamper-resistant hardware with bounded information leakage
Generalized external interaction with tamper-resistant hardware with bounded information leakage
复制标题
与防篡改硬件的广义外部交互，限制信息泄漏
DOI：
10.1145/2517488.2517498
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
S. Devadas
中科院分区：
文献类型：
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作者：
Xiangyao Yu;Christopher W. Fletcher;Ling Ren;Marten van Dijk;S. Devadas
本文研究了与防篡改硬件进行交互的安全方法，这些防篡改硬件泄漏了严格有界的信息量。架构的相互作用机制的支持进行了研究和性能的影响进行评估。交互机制建立在最近提出的安全处理器Ascend[ascend-stc 12]之上。选择Ascend是因为与其他防篡改硬件系统不同，Ascend通过使用不经意RAM（ORAM）和周期性ORAM访问完全混淆pin流量。然而，最初的Ascend建议，除了主存储器之外，只能在程序执行的开始或结束时与外界通信;不允许中间信息传输。我们的系统，Stream-Ascend，是Ascend的扩展，可以实现与外部世界的中间交互。Stream-Ascend在保持相同安全级别的同时，显著提高了Ascend的通用性和效率，仿真结果表明，在智能调度算法下，Stream-Ascend相对于不安全的、理想化的基准处理器的性能开销分别仅为24.5%、0.7%、0.7%在一个大型数据集处理应用程序中，一组流基准测试的结果为3.9%。Stream-Ascend能够以很小的开销为一大类应用程序实现非常高的安全级别。
This paper investigates secure ways to interact with tamper-resistant hardware leaking a strictly bounded amount of information. Architectural support for the interaction mechanisms is studied and performance implications are evaluated. The interaction mechanisms are built on top of a recently-proposed secure processor Ascend[ascend-stc12]. Ascend is chosen because unlike other tamper-resistant hardware systems, Ascend completely obfuscates pin traffic through the use of Oblivious RAM (ORAM) and periodic ORAM accesses. However, the original Ascend proposal, with the exception of main memory, can only communicate with the outside world at the beginning or end of program execution; no intermediate information transfer is allowed. Our system, Stream-Ascend, is an extension of Ascend that enables intermediate interaction with the outside world. Stream-Ascend significantly improves the generality and efficiency of Ascend in supporting many applications that fit into a streaming model, while maintaining the same security level.Simulation results show that with smart scheduling algorithms, the performance overhead of Stream-Ascend relative to an insecure and idealized baseline processor is only 24.5%, 0.7%, and 3.9% for a set of streaming benchmarks in a large dataset processing application. Stream-Ascend is able to achieve a very high security level with small overheads for a large class of applications.