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Bluetooth has become critical as many IoT devices are arriving in the market.Most of the current literature focusing on Bluetooth simulation concentrates onthe network protocols' performances and completely neglects the privacyprotection recommendations introduced in the BLE standard. Indeed, privacyprotection is one of the main issues handled in the Bluetooth standard. Forinstance, the current standard forces devices to change the identifier theyembed within the public and private packets, known as MAC addressrandomization. Although randomizing MAC addresses is intended to preservedevice privacy, recent literature shows many challenges that are still present.One of them is the correlation between the public packets and the emitters.Unfortunately, existing evaluation tools such as NS-3 are not designed toreproduce this Bluetooth standard's essential functionality. This makes itimpossible to test solutions for different device-fingerprinting strategies asthere is a lack of ground truth for large-scale scenarios with the majority ofcurrent BLE devices implementing MAC address randomization. In this paper, wefirst introduce a solution of standard-compliant MAC address randomization inthe NS-3 framework, capable of emulating any real BLE device in the simulationand generating real-world Bluetooth traces. In addition, since the simulationrun-time for trace-collection grows exponentially with the number of devices,we introduce an optimization to linearize public-packet sniffing. This made thelarge-scale trace-collection practically feasible. Then, we use the generatedtraces and associated ground truth to do a case study on the evaluation of ageneric MAC address association available in the literature. Our case studyreveals that close to 90 percent of randomized addresses could be correctlylinked even in highly dense and mobile scenarios.

SimBle: Generating privacy preserving real-world BLE traces with ground truth