How Would Oblivious Memory Boost Graph Analytics on Trusted Processors?
Jiping Yu, Xiaowei Zhu, Kun Chen, Guanyu Feng, Yunyi Chen, Xiaoyu Fan, Wenguang Chen
TL;DR
The paper addresses privacy-preserving multi-party graph analytics on trusted processors, where memory-access patterns can leak sensitive information. It introduces Oblivious Memory (OM) and the ObliGE framework, which use a grid-based, oblivious graph storage and a full-graph scan to avoid costly sorting passes while preserving data-oblivious execution. Key contributions include the 2D grid edge-list storage, vertex mapping via oblivious sort, end-to-end pre/post-processing to maintain obliviousness, and a hardware feasibility discussion with addressable and pinning OM implementations. Empirically, ObliGE achieves up to around 186x speedups over oblivious sort-based baselines and substantially narrows the gap to insecure systems, suggesting OM-enabled trusted processors can make secure graph analytics far more practical and guiding hardware designers toward OM integration.
Abstract
Trusted processors provide a way to perform joint computations while preserving data privacy. To overcome the performance degradation caused by data-oblivious algorithms to prevent information leakage, we explore the benefits of oblivious memory (OM) integrated in processors, to which the accesses are unobservable by adversaries. We focus on graph analytics, an important application vulnerable to access-pattern attacks. With a co-design between storage structure and algorithms, our prototype system is 100x faster than baselines given an OM sized around the per-core cache which can be implemented on existing processors with negligible overhead. This gives insights into equipping trusted processors with OM.