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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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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Wall-PROV: Revisiting Firewall Rule Misconfigurations with Data Provenance and...

Abdullah Al Farooq (Wentworth Institute of Technology), Tanvir Rahman Akash (Trine University), Manash Sarker (Patuakhali Science and Technology University)

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LighTellite: Reinforcement Learning-Based Framework for Energy Efficient Onboard Satellite...

Aviel Ben Siman Tov (Ben Gurion University of the Negev), Edita Grolman (Ben Gurion University of the Negev), Yuval Elovici (Ben Gurion University of the Negev), Asaf Shabtai (Ben Gurion University of the Negev)

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