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

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Cross-gadget tracking has drawn growing consideration from both industrial firms and most people because of its privacy implications and applications for person profiling, customized services, and so forth. One explicit, extensive-used sort of cross-system monitoring is to leverage looking histories of user units, e.g., characterized by a list of IP addresses utilized by the units and domains visited by the units. However, existing shopping history primarily based methods have three drawbacks. First, they cannot capture latent correlations amongst IPs and domains. Second, their performance degrades significantly when labeled device pairs are unavailable. Lastly, they don't seem to be strong to uncertainties in linking looking histories to devices. We suggest GraphTrack, a graph-based mostly cross-gadget tracking framework, to trace customers across different devices by correlating their looking histories. Specifically, we propose to mannequin the complex interplays among IPs, domains, and units as graphs and seize the latent correlations between IPs and between domains. We construct graphs which are sturdy to uncertainties in linking shopping histories to gadgets.

Moreover, we adapt random walk with restart to compute similarity scores between gadgets based mostly on the graphs. GraphTrack leverages the similarity scores to perform cross-gadget tracking. GraphTrack does not require labeled gadget pairs and can incorporate them if out there. We evaluate GraphTrack on two actual-world datasets, i.e., a publicly out there mobile-desktop monitoring dataset (round one hundred customers) and a multiple-gadget tracking dataset (154K customers) we collected. Our results show that GraphTrack considerably outperforms the state-of-the-art on each 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, ItagPro 2022, Nagasaki, Japan. ACM, New York, NY, USA, 15 pages. Cross-machine tracking-a technique used to determine whether numerous gadgets, such as mobile phones and desktops, have frequent homeowners-has drawn a lot attention of each industrial corporations and most people. For example, Drawbridge (dra, 2017), an promoting firm, goes beyond traditional device monitoring to establish gadgets belonging to the same user.

As a result of growing demand for cross-system monitoring and corresponding privacy concerns, the U.S. Federal Trade Commission hosted a workshop (Commission, 2015) in 2015 and ItagPro launched a workers report (Commission, 2017) about cross-machine tracking and industry regulations in early 2017. The growing interest in cross-machine monitoring is highlighted by the privacy implications associated with tracking and iTagPro portable the applications of monitoring for consumer profiling, ItagPro personalised services, and consumer authentication. For example, a financial institution application can undertake cross-system tracking as a part of multi-factor authentication to increase account security. Generally talking, ItagPro cross-machine monitoring primarily leverages cross-system IDs, background setting, or ItagPro shopping historical past of the units. As an example, cross-gadget IDs might embody a user’s e mail tackle or username, which aren't relevant when customers don't register accounts or don't login. Background surroundings (e.g., ultrasound (Mavroudis et al., 2017)) additionally cannot be utilized when units are used in different environments comparable to dwelling and workplace.

Specifically, looking historical past primarily based monitoring utilizes supply and vacation spot pairs-e.g., the shopper IP deal with and the destination website’s domain-of users’ searching information to correlate different devices of the identical person. Several searching history based cross-gadget tracking strategies (Cao et al., 2015; Zimmeck et al., 2017; Malloy et al., 2017) have been proposed. As an example, iTagPro shop IPFootprint (Cao et al., 2015) makes use of supervised learning to analyze the IPs generally utilized by units. Zimmeck et al. (Zimmeck et al., 2017) proposed a supervised method that achieves state-of-the-art efficiency. In particular, their method computes a similarity score by way of Bhattacharyya coefficient (Wang and Pu, 2013) for a pair of devices based mostly on the common IPs and/or domains visited by both gadgets. Then, they use the similarity scores to trace devices. We call the strategy BAT-SU because it makes use of the Bhattacharyya coefficient, where the suffix "-SU" indicates that the tactic is supervised. DeviceGraph (Malloy et al., 2017) is an unsupervised technique that fashions devices as a graph based on their IP colocations (an edge is created between two devices if they used the same IP) and applies community detection for tracking, i.e., the devices in a neighborhood of the graph belong to a person.