Qinhan Tan (Zhejiang University), Zhihua Zeng (Zhejiang University), Kai Bu (Zhejiang University), Kui Ren (Zhejiang University)

Cache conflicts due to deterministic memory-to-cache mapping have long been exploited to leak sensitive information such as secret keys. While randomized mapping is fully investigated for L1 caches, it still remains unresolved about how to secure a much larger last-level cache (LLC). Recent solutions periodically change the mapping strategy to disrupt the crafting of conflicted addresses, which is a critical attack procedure to exploit cache conflicts. Remapping, however, increases both miss rate and access latency. We present PhantomCache for securing an LLC with remapping-free randomized mapping. We propose a localized randomization technique to bound randomized mapping of a memory address within only a limited number of cache sets. The small randomization space offers fast set search over an LLC in a memory access. The intrinsic randomness still suffices to obfuscate conflicts and disrupt efficient exploitation of conflicted addresses. We evaluate PhantomCache against an attacker exploring the state-of-the-art attack with linear-complexity. To secure an 8-bank 16~MB 16-way LLC, PhantomCache confines randomization space of an address within 8 sets and brings only 0.5% performance degradation and 0.5% storage overhead per cache line, which are 3x and 9x more efficient than the state-of-the-art solutions. Moreover, PhantomCache is solely an architectural solution and requires no software change.

View More Papers

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Sinem Sav (EPFL), Apostolos Pyrgelis (EPFL), Juan Ramón Troncoso-Pastoriza (EPFL), David Froelicher (EPFL), Jean-Philippe Bossuat (EPFL), Joao Sa Sousa (EPFL), Jean-Pierre Hubaux (EPFL)

Read More

µRAI: Securing Embedded Systems with Return Address Integrity

Naif Saleh Almakhdhub (Purdue University and King Saud University), Abraham A. Clements (Sandia National Laboratories), Saurabh Bagchi (Purdue University), Mathias Payer (EPFL)

Read More

Demo #10: Security of Deep Learning based Automated Lane...

Takami Sato, Junjie Shen, Ningfei Wang (UC Irvine), Yunhan Jia (ByteDance), Xue Lin (Northeastern University), and Qi Alfred Chen (UC Irvine)

Read More