Common Neighborhood Estimation over Bipartite Graphs under Local Differential Privacy

2024-12-18

Proc. ACM Manag. Data

AI Summary

This paper addresses the problem of estimating the number of common neighbors between vertices on the same side of a bipartite graph under edge-level local differential privacy (edge-LDP), ensuring privacy preservation for relational data. The proposed method introduces a multi-round filtering framework to progressively prune the candidate neighbor set, designs a two-sided neighborhood joint unbiased estimator, and incorporates an adaptive privacy budget allocation mechanism—thereby significantly enhancing robustness against degree skewness and graph sparsity, as well as improving estimation consistency. Extensive experiments on 15 real-world datasets demonstrate that the approach achieves high accuracy and computational efficiency, notably outperforming existing baselines—especially on sparse graphs and those exhibiting heavy-tailed degree distributions.

Technology Category

• Machine Learning: Privacy
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Natural Language Processing: Safety and Robustness

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

Bipartite graphs, formed by two vertex layers, arise as a natural fit for modeling the relationships between two groups of entities. In bipartite graphs, common neighborhood computation between two vertices on the same vertex layer is a basic operator, which is easily solvable in general settings. However, it inevitably involves releasing the neighborhood information of vertices, posing a significant privacy risk for users in real-world applications. To protect edge privacy in bipartite graphs, in this paper, we study the problem of estimating the number of common neighbors of two vertices on the same layer under edge local differential privacy (edge LDP). The problem is challenging in the context of edge LDP since each vertex on the opposite layer of the query vertices can potentially be a common neighbor. To obtain efficient and accurate estimates, we propose a multiple-round framework that significantly reduces the candidate pool of common neighbors and enables the query vertices to construct unbiased estimators locally. Furthermore, we improve data utility by incorporating the estimators built from the neighbors of both query vertices and devise privacy budget allocation optimizations. These improve the estimator's robustness and consistency, particularly against query vertices with imbalanced degrees. Extensive experiments on 15 datasets validate the effectiveness and efficiency of our proposed techniques.

Problem

• Estimating common neighbors in bipartite graphs
• Ensuring edge local differential privacy
• Reducing privacy risks in graph data

Innovation

• Local Differential Privacy
• Multiple-round Framework
• Privacy Budget Optimization