Oblivious Memory Integration Accelerates Graph Analytics on Trusted Processors

Global: Oblivious Memory Integration Accelerates Graph Analytics on Trusted Processors

Researchers led by Jiping Yu and colleagues submitted a study on December 30, 2025, describing a method to enhance graph‑analytics workloads on trusted processors by incorporating oblivious memory (OM). The work targets the privacy‑preserving execution of joint computations while addressing the performance slowdown typical of data‑oblivious algorithms.

Background

Trusted processors, such as Intel SGX and AMD SEV, enable secure enclaves where data can be processed without exposing raw values to the host system. However, the access patterns of algorithms can still leak sensitive information, prompting the use of data‑oblivious techniques that hide memory accesses at the cost of significant runtime overhead.

Oblivious Memory Design

The authors propose embedding an OM module within the processor architecture so that memory accesses become indistinguishable to an adversary. By co‑designing the storage layout and the graph‑analytics algorithms, the system ensures that each operation follows a uniform access pattern regardless of the underlying graph structure.

Performance Evaluation

Experimental results reported in the paper show that the prototype system achieves a speedup of 100× compared with existing oblivious‑algorithm baselines when the OM is sized approximately to the per‑core cache. The authors note that the additional hardware required can be realized on current processors with negligible impact on chip area or power consumption.

Implications for Secure Computing

These findings suggest that integrating OM into trusted processors could make privacy‑preserving graph analytics practical for real‑world applications, such as social‑network analysis or fraud detection, where data sensitivity is paramount.

Future Directions

The study acknowledges that further work is needed to evaluate OM scalability for larger datasets and to explore automated toolchains for co‑designing algorithms with hardware support. The authors also plan to assess the approach against a broader set of attack models.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.