Literature Database Entry

tao2025differentially

Youming Tao, Shuzhen Chen, Kai Li, Dongxiao Yu, Xiuzhen Cheng and Falko Dressler, "Differentially Private Matroid Bandits for Online Network Topology Optimization," Proceedings of 31st ACM International Conference on Mobile Computing and Networking (MobiCom 2025), 20th ACM Workshop on Mobility in the Evolving Internet Architecture (MobiArch 2025), Hong Kong, China, November 2025. (to appear)

Abstract

Efficient and adaptive network topology design is fundamental in communication systems. A key problem is to construct low-cost structures like minimum spanning trees (MSTs) from uncertain and stochastic link measurements. However, topology and link data often reveal sensitive attributes, including location and traffic patterns, posing privacy risks. We propose Differentially Private Matroid Bandits, an online learning framework for consecutive MST construction with formal privacy guarantees. Modeling the problem as stochastic optimization over a matroid, we develop algorithms that balance exploration and exploitation while ensuring differential privacy in both central and local models. We establish regret upper bounds capturing the trade-offs among privacy, accuracy, and structural complexity. Experiments on real network data show our approach achieves near-optimal performance with strong privacy protection.

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Youming Tao
Shuzhen Chen
Kai Li
Dongxiao Yu
Xiuzhen Cheng
Falko Dressler

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