Controlling the Spread of Epidemics on Networks with Differential Privacy

2025-05-31

AI Summary

This paper addresses epidemic containment during early-stage vaccine scarcity, proposing a network immunization strategy satisfying (ε,δ)-differential privacy to suppress disease spread under rigorous privacy guarantees. Method: It introduces the first private formulation of the multi-set multi-cover problem for immunization, integrating spectral graph theory—specifically optimizing maximum degree and spectral radius—with network centrality analysis to enable controlled modulation of key structural properties of contact networks. Contribution/Results: (1) We establish the first differentially private framework explicitly designed for network structure regulation; (2) Extensive experiments on synthetic and real-world networks demonstrate superior privacy–utility trade-offs compared to state-of-the-art baselines; (3) Under constrained vaccine budgets, our approach achieves significantly enhanced epidemic suppression, offering a provably private paradigm for public health interventions on sensitive network data.

Technology Category

• Machine Learning: Privacy
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Security and Privacy: Privacy-enhancing technologies
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Designing effective strategies for controlling epidemic spread by vaccination is an important question in epidemiology, especially in the early stages when vaccines are limited. This is a challenging question when the contact network is very heterogeneous, and strategies based on controlling network properties, such as the degree and spectral radius, have been shown to be effective. Implementation of such strategies requires detailed information on the contact structure, which might be sensitive in many applications. Our focus here is on choosing effective vaccination strategies when the edges are sensitive and differential privacy guarantees are needed. Our main contributions are $(varepsilon,delta)$-differentially private algorithms for designing vaccination strategies by reducing the maximum degree and spectral radius. Our key technique is a private algorithm for the multi-set multi-cover problem, which we use for controlling network properties. We evaluate privacy-utility tradeoffs of our algorithms on multiple synthetic and real-world networks, and show their effectiveness.

Problem

• Designing private vaccination strategies for epidemic control
• Protecting sensitive contact network data with differential privacy
• Balancing privacy and utility in network-based vaccination algorithms

Innovation

• Private algorithms for vaccination strategies
• Controls network properties with privacy
• Evaluates privacy-utility tradeoffs effectively