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.

翻译：SIM卡一直是蜂窝网络用户认证与安全的核心构建模块。虽然它们被设计为蜂窝通信中的隐私保护元件，但却可能成为隐私泄露的根本原因。当前eSIM具有固定的设备配置文件——包含密钥、证书及唯一eUICC标识符——这将设备上配置的每个用户配置文件永久绑定至该设备配置文件。这种绑定使得具备蜂窝运营商视角的攻击者能够将用户身份关联至同一设备，拼凑出完整的生活模式——包括在线行为、移动轨迹及真实身份——即便用户轮换用户身份或采用流量混淆技术。为缓解此问题，我们提出Di5Guise，一种通过解耦设备身份与用户身份从根源打破这种关联的隐私增强架构。Di5Guise的核心是vSIM，一种虚拟化SIM卡，它支持动态设备配置文件配置，使每个用户配置文件可与一个独立、不可关联的设备配置文件相关联。Di5Guise通过确保vSIM在可信状态下运行于安全硬件，与运营商建立信任。我们在现场可编程门阵列（FPGA）板上原型实现Di5Guise，并将其与srsRAN集成，以证明与现有5G基础设施的完全兼容性。使用复杂的用户关联模型，我们证明Di5Guise结合混淆技术可将用户重识别准确率从93%降至49%。