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<p>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.</p>

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Privacy preserving optimization of communication networks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月26日

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  • Dongxu Lei,
  • Xiaotian Lin,
  • Xinghu Yu,
  • Zhihong Zhao,
  • Fangzhou Liu,
  • Yang Shi,
  • Songlin Zhuang,
  • Huijun Gao,
  • Baruch Barzel,
  • Stefano Boccaletti

摘要

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.