Privacy preserving optimization of communication networks

Dongxu Lei, Xiaotian Lin, Xinghu Yu, Zhihong Zhao, Fangzhou Liu, Yang Shi, Songlin Zhuang (), Huijun Gao (), Baruch Barzel and Stefano Boccaletti
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Dongxu Lei: Harbin Institute of Technology
Xiaotian Lin: Yongjiang Laboratory
Xinghu Yu: Ningbo Institute of Intelligent Equipment Technology Company Ltd.
Zhihong Zhao: Ningbo University of Technology
Fangzhou Liu: Harbin Institute of Technology
Yang Shi: University of Victoria
Songlin Zhuang: Yongjiang Laboratory
Huijun Gao: Harbin Institute of Technology
Baruch Barzel: Bar-Ilan University
Stefano Boccaletti: Ningbo University of Technology

Nature Communications, 2025, vol. 16, issue 1, 1-16

Abstract: Abstract Modern society takes connectivity for granted, relying heavily on communication networks, both for interpersonal connection and to support critical infrastructure. As Internet- and data-driven technologies become increasingly pervasive, our dependence on fast, reliable communication will only deepen, necessitating advanced tools for optimizing network efficiency and resilience. Such optimization must account for the interplay between the static network infrastructure and the dynamic user preferences. The challenge is that while the infrastructure data is accessible to network operators, the user preferences, tied to personal mobility and communication habits, are protected by privacy laws and are thus heavily restricted. To address this, we introduce CLUSTER: an interpretable Bayesian nonparametric framework that leverages aggregate, low-resolution, unprotected data to identify user groups with correlated connection patterns. By uncovering these patterns, we show, CLUSTER offers actionable insights, from scheduling base-station activation to guiding deployment of new stations - all without compromising user privacy. CLUSTER thus offers a principled approach to extract meaningful insights from restricted data.

Date: 2025
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DOI: 10.1038/s41467-025-63504-0

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