Preventing Wi-Fi Privacy Leakage: A User Behavioral Similarity Approach

摘要

Mobile devices adopt probe requests to discover nearby Wi- Fi access points (APs) and set up fast Wi-Fi connections. Preferred Network Lists (PNLs) are used to store the lists of connected Wi-Fi APs in the past. Previous studies have shown that such mechanism can lead to serious privacy leakage, for example, attackers can infer users' identity information and movement histories. In this paper, we investigate the privacy issue and propose a data-driven protection strategy. First, we conduct extensive measurement studies based on 27 million users associating with 4 million Wi-Fi APs in 4 cities. We show that probe requests can be used to identify and profile users. Despite that some actions have been taken to reduce privacy leakage (e.g., MAC address randomization), users' PNLs can still be inferred by attackers. Second, we propose a novel privacy protection method, in which users' PNLs are "blurred" by adding faked SSIDs generated using a collaborative filtering algorithm, such that nearby users' PNLs are similar to each other. Finally, we evaluate the performance of our design using real-world Wi-Fi association traces. Our trace-driven simulation shows that the refined PNLs can effectively protect user privacy and ensure fast Wi-Fi connection at the same time.