Loco-Store: Locality-Based Oblivious Data Storage

Wenlong Tian, Ruixuan Li, Zhiyong Xu, Weijun Xiao

Abstract: With the growing popularity of cloud storage, how to prevent information leakage from cloud access patterns attracts great attention. Oblivious RAM is proposed for this purpose. It is designed for the memory system, and most existing work focused on improving performance in the main memory. Recently, ORAM has been extended to the cloud environment, and it is called Oblivious Data Storage. TaoStore, the state-of-the-art oblivious data storage system, integrates the ORAM technology with synchronous I/O technology to reduce the mean response time. As we observed, there is a strong locality existing in user accesses. However, existing Oblivious Storage research did not consider this. In this article, we propose Loco-Store, an oblivious data storage. In Loco-Store, we design a novel stash controller scheme that can dynamically group relevant blocks during the oblivious I/O processes. We also propose a locality-based eviction algorithm to keep the security guarantee. The theoretical proof proves that our scheme keeps the security definition of ORAM. Finally, we implement a prototype and conduct extensive experiments on real-world datasets. The results show that Loco-Store can save the network bandwidth consumption up to 39.19 percent, and reduce the overall access time by 26.17 percent