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Cross-gadget tracking has drawn growing consideration from both business corporations and most people because of its privacy implications and purposes for consumer profiling, personalized providers, iTagPro and many others. One particular, iTagPro smart tracker large-used type of cross-system tracking is to leverage looking histories of consumer gadgets, e.g., characterized by a list of IP addresses used by the units and domains visited by the devices. However, existing looking historical past primarily based methods have three drawbacks. First, they can't capture latent correlations amongst IPs and domains. Second, their efficiency degrades considerably when labeled machine pairs are unavailable. Lastly, they don't seem to be sturdy to uncertainties in linking searching histories to units. We propose GraphTrack, a graph-based cross-gadget monitoring framework, to track customers throughout completely different gadgets by correlating their browsing histories. Specifically, we suggest to mannequin the advanced interplays amongst IPs, domains, and devices as graphs and capture the latent correlations between IPs and between domains. We construct graphs which might be robust to uncertainties in linking shopping histories to units.

Moreover, we adapt random walk with restart to compute similarity scores between gadgets based on the graphs. GraphTrack leverages the similarity scores to carry out cross-device monitoring. GraphTrack does not require labeled device pairs and might incorporate them if obtainable. We evaluate GraphTrack on two real-world datasets, i.e., iTagPro smart tracker a publicly out there mobile-desktop tracking dataset (round a hundred customers) and a a number of-system monitoring dataset (154K customers) we collected. Our results show that GraphTrack substantially outperforms the state-of-the-artwork on each datasets. ACM Reference Format: Binghui Wang, Tianchen Zhou, Song Li, Yinzhi Cao, Neil Gong. 2022. GraphTrack: A Graph-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-system monitoring-a technique used to determine whether or not various gadgets, comparable to cellphones and desktops, have frequent owners-has drawn a lot attention of both commercial companies and most people. For instance, Drawbridge (dra, 2017), an promoting company, goes past traditional system monitoring to determine devices belonging to the same user.

As a result of rising demand for cross-device tracking and corresponding privacy issues, the U.S. Federal Trade Commission hosted a workshop (Commission, 2015) in 2015 and released a workers report (Commission, 2017) about cross-machine monitoring and trade rules in early 2017. The growing curiosity in cross-machine tracking is highlighted by the privacy implications associated with tracking and the functions of tracking for user profiling, iTagPro portable customized companies, and consumer authentication. For instance, a financial institution application can adopt cross-gadget tracking as part of multi-factor authentication to increase account security. Generally talking, cross-system tracking mainly leverages cross-gadget IDs, background setting, or searching history of the devices. As an example, cross-system IDs could embrace a user’s e-mail tackle or username, which are not applicable when users don't register accounts or don't login. Background surroundings (e.g., ultrasound (Mavroudis et al., 2017)) additionally cannot be applied when gadgets are used in different environments resembling residence and office.

Specifically, searching historical past based mostly monitoring utilizes source and destination pairs-e.g., the client IP handle and the vacation spot website’s domain-of users’ searching data to correlate different gadgets of the identical user. Several browsing historical past based mostly cross-device tracking strategies (Cao et al., 2015; Zimmeck et al., 2017; Malloy et al., 2017) have been proposed. As an illustration, IPFootprint (Cao et al., 2015) uses supervised studying to analyze the IPs generally utilized by devices. Zimmeck et al. (Zimmeck et al., 2017) proposed a supervised method that achieves state-of-the-artwork efficiency. In particular, their methodology computes a similarity rating through Bhattacharyya coefficient (Wang and Pu, 2013) for a pair of units based on the widespread IPs and/or domains visited by both gadgets. Then, they use the similarity scores to track units. We call the tactic BAT-SU because it uses the Bhattacharyya coefficient, the place the suffix "-SU" indicates that the strategy is supervised. DeviceGraph (Malloy et al., 2017) is an unsupervised methodology that fashions gadgets as a graph primarily based on their IP colocations (an edge is created between two units in the event that they used the same IP) and applies neighborhood detection for tracking, i.e., the devices in a community of the graph belong to a consumer.