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.

翻译：可信处理器为执行联合计算同时保护数据隐私提供了一种途径。为克服数据无感知算法为防止信息泄露而导致的性能下降，我们探索了集成于处理器中的无感知内存（OM）的优势，其访问模式对攻击者不可观测。我们聚焦于图分析这一易受访问模式攻击的重要应用领域。通过存储结构与算法的协同设计，在配备与单核缓存容量相当的无感知内存（该设计可在现有处理器上以可忽略的开销实现）的条件下，我们的原型系统比基准方案快100倍。这为在可信处理器中集成无感知内存提供了重要启示。