GORAM: Graph-oriented ORAM for Efficient Ego-centric Queries on Federated Graphs
Xiaoyu Fan, Kun Chen, Jiping Yu, Xiaowei Zhu, Yunyi Chen, Huanchen, Zhang, Wei Xu

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…
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Taxonomy
TopicsCaching and Content Delivery · Graph Theory and Algorithms · Privacy-Preserving Technologies in Data
