Controlling The Spread of Epidemics on Networks with Differential Privacy

Dung Nguyen, Aravind Srinivasan, Renata Valieva, Anil Vullikanti, Jiayi Wu

Keywords: Differential privacy, network science, computational epidemiology, public health

TL;DR: Algorithms to Contain the Spread of Epidemics by Reducing the Maximum Degree and Spectral Radius under Differential Privacy.

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.

Primary Area: Social and economic aspects of machine learning (e.g., fairness, interpretability, human-AI interaction, privacy, safety, strategic behavior)

Submission Number: 24883