DR.SGX: Hardening SGX Enclaves against Cache Attacks with Data Location Randomization
This addresses security for users of SGX enclaves by offering a practical defense against side-channel attacks, though it is an incremental improvement over existing defenses.
The paper tackles the vulnerability of Intel's SGX to cache-based side-channel attacks by proposing data location randomization to break the link between observable memory patterns and actual data accesses, resulting in a tool called DR.SGX that provides adjustable protection with no developer assistance.
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.