Efficient Association of Wi-Fi Probe Requests under MAC Address Randomization

Abstract

Wi-Fi-enabled devices such as smartphones periodically search for available networks by broadcasting probe requests which encapsulate MAC addresses as the device identifiers. To protect privacy (user identity and location), modern devices embed random MAC addresses in their probe frames, the so-called MAC address randomization. Such randomization greatly hampers statistical analysis such as people counting and trajectory inference. To mitigate its impact while respecting privacy, we propose Espresso, a simple, novel and efficient approach which establishes probe request association under MAC address randomization. Espresso models the frame association as a flow network, with frames as nodes and frame correlation as edge cost. To estimate the correlation between any two frames, it considers the multimodality of request frames, including information elements, sequence numbers and received signal strength. It then associates frames with minimum-cost flow optimization. To the best of our knowledge, this is the first piece of work that formulates the probe request association problem as network flow optimization using frame correlation. We have implemented Espresso and conducted extensive experiments in a leading shopping mall. Our results show that Espresso outperforms the state-of-the-art schemes in terms of discrimination accuracy (> 80%) and V-measure scores (> 0.85).