GORAM: Graph-oriented ORAM for Efficient Ego-centric Queries on Federated Graphs

TL;DR

This paper introduces GORAM, a privacy-preserving graph data structure enabling efficient ego-centric queries on large federated graphs using MPC, achieving practical performance on billion-scale graphs.

Contribution

GORAM is the first graph-oriented ORAM-based structure that enables privacy-preserving ego-centric queries on large federated graphs with practical efficiency.

Findings

Queries completed in 58.1 ms to 35.7 s on billion-scale graphs.

Supports five common ego-centric query types.

Demonstrates practical performance on real-world MPC framework.

Abstract

Ego-centric queries, focusing on a target vertex and its direct neighbors, are essential for various applications. Enabling such queries on graphs owned by mutually distrustful data providers, without breaching privacy, holds promise for more comprehensive results. In this paper, we propose GORAM, a graph-oriented data structure that enables efficient ego-centric queries on federated graphs with strong privacy guarantees. GORAM is built upon secure multi-party computation (MPC) and ensures that no single party can learn any sensitive information about the graph data or the querying keys during the process. However, achieving practical performance with privacy guaranteed presents a challenge. To overcome this, GORAM is designed to partition the federated graph and construct an Oblivious RAM(ORAM)-inspired index atop these partitions. This design enables each ego-centric query to process only a single partition, which can be accessed fast and securely. To evaluate the performance of GORAM, we developed a prototype querying engine on a real-world MPC framework. We conduct a comprehensive evaluation with five commonly used queries on both synthetic and real-world graphs. Our evaluation shows that all benchmark queries can be completed in just 58.1 milliseconds to 35.7 seconds, even on graphs with up to 41.6 million vertices and 1.4 billion edges. To the best of our knowledge, this represents the first instance of processing billion-scale graphs with practical performance on MPC.