How Would Oblivious Memory Boost Graph Analytics on Trusted Processors?

TL;DR

This paper explores integrating oblivious memory into trusted processors to significantly improve the performance of privacy-preserving graph analytics, achieving up to 100x speedup with minimal overhead.

Contribution

It demonstrates a co-designed system combining storage and algorithms that leverages oblivious memory to accelerate graph analytics on trusted processors.

Findings

Prototype system is 100x faster than baselines.

Oblivious memory can be integrated with negligible overhead.

Provides insights into enhancing trusted processors with OM.

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.