Exploiting data locality in memory for ORAM to reduce memory access overheads
Jinxi Kuang, Minghua Shen, Yutong Lu, Nong Xiao · 2022
This paper proposes a locality-aware Oblivious RAM (ORAM) primitive, named Green ORAM, which exploits spatial locality of data in the physical memory for reducing ORAM overheads. The Green ORAM is novel consisting of three policies. The first is row-guided label allocation used for mapping spatial locality onto ORAM tree to reduce the number of memory commands. The second is segment-based path replacement able to improve the data locality within the path in the ORAM tree in order to remove the redundant memory accesses. The third is multi-path write-back able to improve the data locality between different paths in order to obtain theoretical best stash hit rate. Notably, the Green ORAM still maintains the security as we analyzed. Experimental results show that Green ORAM achieves a 28.72% access latency reduction, and a 19.06% memory energy consumption reduction on average, compared with the state-of-the-art String ORAM.