SCALE: Secure and Scalable Cache Partitioning

Dynamically partitioned last-level caches enhance performance while also introducing security vulnerabilities. We show how cache allocation policies can act as a side-channel and be exploited to launch attacks and obtain sensitive information. Our analysis reveals that information leaks due to predictable changes in cache allocation for the victim, that is caused and/or observed by the adversary, leads to exploits. We propose SCALE, a secure cache allocation policy and enforcement mechanism, to protect the cache against timing-based side-channel attacks. SCALE uses randomness, in a novel way, to enable dynamic and scalable partitioning while protecting against cache allocation policy side-channel attacks. Non-determinism is introduced into the allocation policy decisions by adding noise, which prevents the adversary from observing predictable changes in allocation and thereby infer secrets. We leverage differential privacy (DP), and show that SCALE can provide quantifiable and information theoretic security guarantees. SCALE outperforms state-of-the-art secure cache solutions, on a 16-core tiled chip multi-processor (CMP) with multi-programmed workloads, and improves performance up to 39% and by 14%, on average.