Camouflage: Utility-Aware Obfuscation for Accurate Simulation of Sensitive Program Traces

IF 1.5 3区 计算机科学 Q4 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Asmita Pal, Keerthana Desai, Rahul Chatterjee, Joshua San Miguel
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引用次数: 0

Abstract

Trace-based simulation is a widely used methodology for system design exploration. It relies on realistic traces that represent a range of behaviors necessary to be evaluated, containing a lot of information about the application, its inputs and the underlying system on which it was generated. Consequently, generating traces from real-world executions risk leakage of sensitive information. To prevent this, traces can be obfuscated before release. However, this can undermine their ideal utility, i.e., how realistically a program behavior was captured. To address this, we propose Camouflage, a novel obfuscation framework, designed with awareness of the necessary architectural properties required to preserve trace utility, while ensuring secrecy of the inputs used to generate the trace. Focusing on memory access traces, our extensive evaluation on various benchmarks shows that camouflaged traces preserve the performance measurements of the original execution, with an average τ correlation of 0.66. We model input secrecy as an input indistinguishability problem and show that the average security loss is 7.8%, which is better than traces generated from the state-of-the-art.

伪装:为准确模拟敏感程序痕迹而进行的效用意识混淆
基于轨迹的仿真是一种广泛应用的系统设计探索方法。它依赖于真实的轨迹,这些轨迹代表了一系列需要评估的行为,包含了大量有关应用程序、其输入以及生成轨迹的底层系统的信息。因此,从现实世界的执行中生成跟踪信息有可能泄露敏感信息。为防止这种情况发生,可在发布前对跟踪信息进行混淆处理。然而,这会损害其理想效用,即程序行为被捕获的真实程度。为了解决这个问题,我们提出了一种新颖的混淆框架--Camouflage,它在设计时考虑到了保留跟踪效用所需的必要架构特性,同时确保用于生成跟踪的输入保密。我们以内存访问跟踪为重点,对各种基准进行了广泛评估,结果表明,经过伪装的跟踪能保持原始执行的性能测量,平均 τ 相关性为 0.66。我们将输入保密建模为输入不可区分性问题,结果表明平均安全损失为 7.8%,优于最先进技术生成的痕迹。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACM Transactions on Architecture and Code Optimization
ACM Transactions on Architecture and Code Optimization 工程技术-计算机:理论方法
CiteScore
3.60
自引率
6.20%
发文量
78
审稿时长
6-12 weeks
期刊介绍: ACM Transactions on Architecture and Code Optimization (TACO) focuses on hardware, software, and system research spanning the fields of computer architecture and code optimization. Articles that appear in TACO will either present new techniques and concepts or report on experiences and experiments with actual systems. Insights useful to architects, hardware or software developers, designers, builders, and users will be emphasized.
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