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.

翻译：自我中心查询聚焦于目标顶点及其直接邻接点，是众多应用中的关键操作。在互不信任的数据提供方所拥有的图上实现此类查询，同时确保隐私不被泄露，有望获得更全面的分析结果。本文提出GORAM，一种面向联邦图的图导向数据结构，能够在强隐私保证下实现高效的自我中心查询。GORAM基于安全多方计算（MPC）构建，确保在整个查询过程中没有任何单一参与方能获取图数据或查询密钥的敏感信息。然而，在保证隐私的前提下实现实用性能仍面临挑战。为此，GORAM通过划分联邦图并在分区之上构建受不经意随机存取存储器（ORAM）启发的索引结构来解决该问题。该设计使得每个自我中心查询仅需处理单个分区，从而实现快速且安全的访问。为评估GORAM的性能，我们在真实MPC框架上开发了原型查询引擎，并在合成图与现实图数据上对五种常用查询进行了全面测试。实验表明，即使在包含4160万个顶点和14亿条边的大规模图上，所有基准查询均能在58.1毫秒至35.7秒内完成。据我们所知，这是首次在MPC环境下以实用性能处理十亿级规模图数据的实例。