arXiv2022
Cross-device tracking has drawn growing attention from both commercial companies and the general public because of its privacy implications and applications for user profiling, personalized services, etc. One particular, wide-used type of cross-device tracking is to leverage browsing histories of user devices, e.g., characterized by a list of IP addresses used by the devices and domains visited by the devices. However, existing browsing history based methods have three drawbacks. First, they cannot capture latent correlations among IPs and domains. Second, their performance degrades significantly when labeled device pairs are unavailable. Lastly, they are not robust to uncertainties in linking browsing histories to devices. We propose GraphTrack, a graph-based cross-device tracking framework, to track users across different devices by correlating their browsing histories. Specifically, we propose to model the complex interplays among IPs, domains, and devices as graphs and capture the latent correlations between IPs and between domains. We construct graphs that are robust to uncertainties in linking browsing histories to devices. Moreover, we adapt random walk with restart to compute similarity scores between devices based on the graphs. GraphTrack leverages the similarity scores to perform cross-device tracking. GraphTrack does not require labeled device pairs and can incorporate them if available. We evaluate GraphTrack on two real-world datasets, i.e., a publicly available mobile-desktop tracking dataset (around 100 users) and a multiple-device tracking dataset (154K users) we collected. Our results show that GraphTrack substantially outperforms the state-of-the-art on both datasets.