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Recent research has demonstrated that Intel's SGX is vulnerable tosoftware-based side-channel attacks. In a common attack, the adversary monitorsCPU caches to infer secret-dependent data accesses patterns. Known defenseshave major limitations, as they require either error-prone developerassistance, incur extremely high runtime overhead, or prevent only specificattacks. In this paper, we propose data location randomization as a noveldefense against side-channel attacks that target data access patterns. Our goalis to break the link between the memory observations by the adversary and theactual data accesses by the victim. We design and implement a compiler-basedtool called DR.SGX that instruments the enclave code, permuting data locationsat fine granularity. To prevent correlation of repeated memory accesses weperiodically re-randomize all enclave data. Our solution requires no developerassistance and strikes the balance between side-channel protection andperformance based on an adjustable security parameter.

DR.SGX: Hardening SGX Enclaves against Cache Attacks with Data Location Randomization