Di5Guise: 5G Privacy with vSIM

• URL: http://arxiv.org/abs/2606.16943v1
• Date: Mon, 15 Jun 2026 16:44:40 GMT
• Title: Di5Guise: 5G Privacy with vSIM
• Abstract summary: Di5Guise is a privacy-enhancing architecture that breaks this correlation at its root by decoupling the device identity from the subscriber identity.<n>We show that Di5Guise reduces user re-identification from 93% to 49% when combined with obfuscation.
• Score: 0.769971486557519
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: SIM cards have been the key building block of user authenticationand security in cellular networks. While they are meant to serve as privacy protecting elements in cellular communications, they can be the root cause of privacy loss. Current eSIMs come with a fixed device profile--comprising a secret key, a certificate, and a unique eUICC identifier--that permanently binds every subscriber profile provisioned on the device to that device profile. This binding enables an attacker with the vantage point of a cellular operator to correlate subscriber identities back to a single device, piecing together a complete pattern of life--online activities, movement patterns, and real-world identity--even when users rotate subscriber identities or employ traffic obfuscation techniques. To mitigate this concern, we introduce Di5Guise, a privacy-enhancing architecture that breaks this correlation at its root by decoupling the device identity from the subscriber identity. Central to Di5Guise is vSIM, a virtualized SIM card that enables dynamic device profile provisioning, allowing each subscriber profile to be associated with a distinct, unlinkable device profile. Di5Guise establishes trust with the operator by ensuring that vSIM is running on secure hardware in a trustworthy state. We prototype Di5Guise on a Field Programmable Gate Array (FPGA) board and integrate it with srsRAN to demonstrate full compatibility with existing 5G infrastructure. Using a complex user correlation model, we show that Di5Guise reduces user re-identification accuracy from 93% to 49% when combined with obfuscation.

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