GraphTrack: A Graph-Based Cross-Device Tracking Framework

Cross-machine monitoring has drawn growing consideration from both industrial corporations and the general public because of its privateness implications and applications for user profiling, personalized services, etc. One explicit, extensive-used sort of cross-machine monitoring is to leverage looking histories of person units, e.g., characterized by a list of IP addresses used by the units and domains visited by the devices. However, present shopping historical past based methods have three drawbacks. First, they cannot capture latent correlations among IPs and domains. Second, their performance degrades significantly when labeled machine pairs are unavailable. Lastly, they are not strong to uncertainties in linking shopping histories to devices. We suggest GraphTrack, a graph-based mostly cross-machine monitoring framework, to trace users across completely different devices by correlating their browsing histories. Specifically, we suggest to mannequin the complicated interplays amongst IPs, domains, and gadgets as graphs and capture the latent correlations between IPs and between domains. We construct graphs which are strong to uncertainties in linking browsing histories to gadgets.



Moreover, we adapt random stroll with restart to compute similarity scores between units primarily based on the graphs. GraphTrack leverages the similarity scores to perform cross-machine tracking. GraphTrack doesn't require labeled gadget pairs and may incorporate them if accessible. We consider GraphTrack on two actual-world datasets, i.e., a publicly out there cell-desktop monitoring dataset (around 100 customers) and a multiple-system monitoring dataset (154K users) we collected. Our outcomes present that GraphTrack substantially 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-based mostly Cross-Device Tracking Framework. In Proceedings of the 2022 ACM Asia Conference on Computer and iTagPro Device Communications Security (ASIA CCS ’22), May 30-June 3, 2022, iTagPro Device Nagasaki, Japan. ACM, New York, NY, USA, 15 pages. Cross-gadget monitoring-a way used to identify whether or iTagPro Device not numerous gadgets, corresponding to cellphones and desktops, have common owners-has drawn much consideration of each business firms and the general public. For example, Drawbridge (dra, 2017), an advertising company, goes past traditional device tracking to determine gadgets belonging to the same consumer.



As a result of rising demand for cross-iTagPro Device monitoring and corresponding privacy considerations, the U.S. Federal Trade Commission hosted a workshop (Commission, 2015) in 2015 and released a workers report (Commission, 2017) about cross-device tracking and business rules in early 2017. The rising interest in cross-system tracking is highlighted by the privateness implications related to tracking and iTagPro Device the applications of monitoring for person profiling, customized services, and consumer authentication. For example, a bank software can undertake cross-device tracking as a part of multi-factor authentication to extend account safety. Generally talking, cross-system monitoring mainly leverages cross-gadget IDs, background environment, or iTagPro Product shopping history of the units. For example, cross-machine IDs might embody a user’s e mail address or username, which aren't relevant when users do not register accounts or iTagPro Device do not login. Background surroundings (e.g., ultrasound (Mavroudis et al., 2017)) additionally cannot be applied when devices are used in different environments reminiscent of home and workplace.