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.

基于迹线的仿真是一种广泛使用的系统设计探索方法。它依赖于代表一系列需要评估的行为的真实痕迹，其中包含有关应用程序、其输入以及生成该应用程序的底层系统的大量信息。因此，从现实世界的执行中生成痕迹存在泄露敏感信息的风险。为了防止这种情况，可以在发布之前对痕迹进行混淆。然而，这可能会破坏它们的理想效用，即捕获程序行为的真实性。为了解决这个问题，我们提出了 Camouflage，一种新颖的混淆框架，其设计时考虑到保留跟踪实用性所需的必要架构属性，同时确保用于生成跟踪的输入的保密性。我们重点关注内存访问轨迹，对各种基准测试的广泛评估表明，伪装轨迹保留了原始执行的性能测量值，平均τ相关性为 0.66。我们将输入保密性建模为输入不可区分性问题，并表明平均安全损失为 7.8%，这比最先进的技术生成的跟踪要好。