GraphTrack: a Graph-Primarily Based Cross-Device Tracking Framework > 자유게시판

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Cross-device monitoring has drawn growing attention from each business companies and most of the people because of its privateness implications and applications for user profiling, personalised providers, and many others. One particular, vast-used kind of cross-system monitoring is to leverage searching histories of consumer units, e.g., characterized by a listing of IP addresses used by the gadgets and domains visited by the devices. However, present browsing historical past based mostly strategies have three drawbacks. First, they cannot seize latent correlations among IPs and iTagPro portable domains. Second, their efficiency degrades significantly when labeled gadget pairs are unavailable. Lastly, they are not robust to uncertainties in linking looking histories to devices. We suggest GraphTrack, a graph-based mostly cross-device monitoring framework, to trace users throughout totally different units by correlating their browsing histories. Specifically, we suggest to model the complex interplays amongst IPs, iTagPro bluetooth tracker domains, and gadgets as graphs and capture the latent correlations between IPs and between domains. We assemble graphs which can be robust to uncertainties in linking searching histories to units.

Moreover, we adapt random walk with restart to compute similarity scores between gadgets primarily based on the graphs. GraphTrack leverages the similarity scores to carry out cross-system monitoring. GraphTrack does not require labeled system pairs and might incorporate them if available. We consider GraphTrack on two real-world datasets, i.e., a publicly out there cellular-desktop tracking dataset (round one hundred customers) and a a number of-machine 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 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 tracking-a way used to identify whether or not various gadgets, resembling cell phones and desktops, have widespread house owners-has drawn much attention of both business firms and most of the people. For instance, Drawbridge (dra, 2017), an advertising company, goes past conventional gadget tracking to determine units belonging to the same consumer.

Due to the growing demand luggage tracking device for cross-machine monitoring and ItagPro corresponding privateness considerations, the U.S. Federal Trade Commission hosted a workshop (Commission, luggage tracking device 2015) in 2015 and launched a employees report (Commission, 2017) about cross-system monitoring and business rules in early 2017. The rising curiosity in cross-machine monitoring is highlighted by the privateness implications related to tracking and the purposes of tracking for user profiling, customized companies, and consumer authentication. For instance, a bank software can adopt cross-device luggage tracking device as a part of multi-issue authentication to increase account security. Generally speaking, cross-gadget tracking mainly leverages cross-machine IDs, background surroundings, or shopping historical past of the gadgets. For instance, cross-machine IDs may include a user’s e mail deal with or username, which aren't relevant when customers do not register accounts or do not login. Background atmosphere (e.g., ultrasound (Mavroudis et al., luggage tracking device 2017)) also can't be utilized when units are used in different environments resembling residence and workplace.

Specifically, shopping historical past based mostly monitoring utilizes supply and vacation spot pairs-e.g., the shopper IP deal with and the vacation spot website’s area-of users’ searching information to correlate completely different devices of the identical person. Several browsing history primarily based cross-machine monitoring methods (Cao et al., 2015; Zimmeck et al., iTagPro geofencing 2017; Malloy et al., 2017) have been proposed. For instance, IPFootprint (Cao et al., 2015) uses supervised studying to analyze the IPs generally used by units. Zimmeck et al. (Zimmeck et al., 2017) proposed a supervised methodology that achieves state-of-the-artwork performance. Specifically, their methodology computes a similarity rating via Bhattacharyya coefficient (Wang and Pu, 2013) for a pair of devices based mostly on the widespread IPs and/or domains visited by each units. Then, they use the similarity scores to track gadgets. We call the tactic BAT-SU since it makes use of the Bhattacharyya coefficient, where the suffix "-SU" indicates that the tactic is supervised. DeviceGraph (Malloy et al., iTagPro 2017) is an unsupervised technique that fashions units as a graph based on their IP colocations (an edge is created between two devices in the event that they used the same IP) and applies neighborhood detection for tracking, i.e., the devices in a group of the graph belong to a person.