GraphTrack: A Graph-Based Cross-Device Tracking Framework

Cross-system monitoring has drawn rising attention from both business companies and most people due to its privacy implications and applications for Tagsley smart tracker person profiling, customized providers, and so on. One specific, vast-used type of cross-system monitoring is to leverage browsing histories of user gadgets, e.g., characterized by a listing of IP addresses used by the gadgets and domains visited by the devices. However, existing shopping historical past based mostly strategies have three drawbacks. First, they can not seize latent correlations among IPs and domains. Second, their performance degrades significantly when labeled device pairs are unavailable. Lastly, they are not sturdy to uncertainties in linking searching histories to devices. We propose GraphTrack, a graph-primarily based cross-machine monitoring framework, to trace users across completely different units by correlating their looking histories. Specifically, we suggest to model the complicated interplays amongst IPs, domains, and Tagsley smart tracker gadgets as graphs and capture the latent correlations between IPs and between domains. We assemble graphs that are robust to uncertainties in linking searching histories to gadgets.



Moreover, we adapt random walk with restart to compute similarity scores between units primarily based on the graphs. GraphTrack leverages the similarity scores to carry out cross-system tracking. GraphTrack does not require labeled system pairs and can incorporate them if available. We evaluate GraphTrack on two real-world datasets, i.e., a publicly available cell-desktop monitoring dataset (around one hundred customers) and a multiple-machine monitoring dataset (154K customers) we collected. Our outcomes show that GraphTrack considerably outperforms the state-of-the-artwork on both datasets. ACM Reference Format: Binghui Wang, Tianchen Zhou, Song Li, Yinzhi Cao, Neil Gong. 2022. GraphTrack: A Graph-primarily based Cross-Device Tracking Framework. In Proceedings of the 2022 ACM Asia Conference on Computer and Communications Security (ASIA CCS ’22), May 30-June 3, 2022, Nagasaki, Japan. ACM, New York, NY, USA, 15 pages. Cross-machine monitoring-a method used to identify whether numerous devices, equivalent to cellphones and desktops, have frequent homeowners-has drawn a lot attention of each commercial corporations and most people. For Tagsley tracking card example, Drawbridge (dra, 2017), an advertising firm, goes beyond traditional system monitoring to determine devices belonging to the same consumer.



Due to the rising demand for cross-device tracking and corresponding privateness considerations, the U.S. Federal Trade Commission hosted a workshop (Commission, 2015) in 2015 and released a employees report (Commission, 2017) about cross-gadget monitoring and trade regulations in early 2017. The rising interest in cross-device monitoring is highlighted by the privateness implications associated with tracking and the applications of monitoring for person profiling, Tagsley smart tracker personalized companies, and consumer authentication. For instance, a bank software can undertake cross-gadget monitoring as a part of multi-issue authentication to extend account security.