Hexuan Yu (Virginia Polytechnic Institute and State University), Chaoyu Zhang (Virginia Polytechnic Institute and State University), Yang Xiao (University of Kentucky), Angelos D. Keromytis (Georgia Institute of Technology), Y. Thomas Hou (Virginia Polytechnic Institute and State University), Wenjing Lou (Virginia Polytechnic Institute and State University)

Mobile Network Operators (MNOs) are known to leak or sell subscribers’ sensitive information, including geolocation and communication histories. Anonymous mobile user authentication methods, such as [48] (USENIX Sec’21), [55] (NDSS’24), [13] (CCS’24), [54] (S&P’25), enable users to access mobile networks without revealing long-term identifiers like phone numbers or Subscription Permanent Identifiers (SUPI).

However, the absence of identity transparency and location awareness poses significant challenges to implementing the above anonymous access methods in real-world mobile networks, particularly for supporting essential functions such as call routing, usage measurement, and charging. To overcome these limitations, we propose ANONYCALL, a privacy-preserving call management architecture that supports anonymous mobile network access while enabling two essential functions: anonymous callee discovery and usage-based charging. The anonymous callee discovery function incorporates an out-of-band authentication mechanism to securely share temporary callee identifiers with the caller, allowing the latter to establish native calls without obtaining the callee’s permanent information. The usage-based charging function introduces an anonymous and accountable balance credential that enables accurate charging and prevents double-spending while preserving mobile user anonymity. Fully compatible with existing mobile networks, ANONYCALL introduces minimal overhead, adding less than 200 ms to call establishment. Evaluations with smartphones and standard calling systems demonstrate its practicality, offering a viable solution for privacy-preserving yet functional mobile communication.

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KnowHow: Automatically Applying High-Level CTI Knowledge for Interpretable and...

Yuhan Meng (Key Laboratory of High-Confidence Software Technologies (MOE), School of Computer Science, Peking University), Shaofei Li (Key Laboratory of High-Confidence Software Technologies (MOE), School of Computer Science, Peking University), Jiaping Gui (School of Computer Science, Shanghai Jiao Tong University), Peng Jiang (Southeast University), Ding Li (Key Laboratory of High-Confidence Software Technologies (MOE), School of…

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Chasing Shadows: Pitfalls in LLM Security Research

Jonathan Evertz (CISPA Helmholtz Center for Information Security), Niklas Risse (Max Planck Institute for Security and Privacy), Nicolai Neuer (Karlsruhe Institute of Technology), Andreas Müller (Ruhr University Bochum), Philipp Normann (TU Wien), Gaetano Sapia (Max Planck Institute for Security and Privacy), Srishti Gupta (Sapienza University of Rome), David Pape (CISPA Helmholtz Center for Information Security),…

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Private Yet Accurate: A Decentralized Approach to System Intrusion...

Jinghan Zhang (University of Virginia), Mati Ur Rehman (University of Virginia), Sharon Biju (University of Virginia), Saleha Muzammil (University of Virginia), Wajih Ul Hassan (University of Virginia)

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