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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Amrita Roy Chowdhury (University of Michigan, Ann Arbor), David Glukhov (University of Toronto and Vector Institute), Divyam Anshumaan (University of Wisconsin-Madison), Prasad Chalasani (Langroid Incorporated), Nicholas Papernot (University of Toronto and Vector Institute), Somesh Jha (University of Wisconsin-Madison), Mihir Bellare (University of California, San Diego)

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Minkyung Park (University of Texas at Dallas), Zelun Kong (University of Texas at Dallas), Dave (Jing) Tian (Purdue University), Z. Berkay Celik (Purdue University), Chung Hwan Kim (University of Texas at Dallas)

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