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

2024-10-03

arXiv.org

AI Summary

Privacy-preserving querying over jointly owned, mutually untrusted graph data remains challenging. Method: This paper proposes the first practical federated ego-centric query scheme for billion-scale graphs. It introduces ORAM principles into graph structural design for the first time, constructing a partitioned, graph-oriented ORAM index; further, it implements a customized oblivious memory access protocol via secure multi-party computation (MPC), ensuring full concealment of graph topology, vertex attributes, and query keys from all participants. Contribution/Results: Under strong MPC-based privacy guarantees, the scheme achieves fast single-partition query execution. Evaluated on a real-world graph with 41.6 million vertices and 1.4 billion edges, five canonical ego-centric queries complete in 58.1 ms–35.7 seconds—substantially outperforming existing privacy-preserving graph query methods.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Search and Optimization: Distributed Search

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

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.

Problem

• Privacy-Preserving Queries
• Graph Data
• Trusted Data Sharing Environment

Innovation

• GORAM
• Secure Multi-party Graph Querying
• Large-scale Graph Data Efficiency