35th Annual Computer Security Applications Conference (ACSAC 2019)

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DR.SGX: Automated and Adjustable Side-Channel Protection for SGX using Data Location Randomization

Recent research has demonstrated that Intel's SGX is vulnerable to software-based side-channel attacks. In a common attack, the adversary monitors CPU caches to infer secret-dependent data accesses patterns. Known defenses have major limitations, as they require either error-prone developer assistance, incur extremely high runtime overhead, or prevent only specific attacks.

In this paper, we propose data location randomization as a novel defense against side-channel attacks that target data access patterns. Our goal is to break the link between the memory observations by the adversary and the actual data accesses by the victim. We design and implement a compiler-based tool called DR.SGX that instruments the enclave code, permuting data locations at fine granularity. To prevent correlation of repeated memory accesses we periodically re-randomize all enclave data. Our solution requires no developer assistance and strikes the balance between side-channel protection and performance based on an adjustable security parameter.

Ferdinand Brasser
Technische Universitat Darmstadt

Srdjan Capkun
ETH Zurich

Alexandra Dmitrienko
University of Wurzburg

Tommaso Frassetto
Technische Universitat Darmstadt

Kari Kostiainen
ETH Zurich

Ahmad-Reza Sadeghi
Technische Universitat Darmstadt


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