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Like a pack of wolves: Community structure of web trackers
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.ORCID iD: 0000-0001-8219-4862
2016 (English)In: 17th International Conference on Passive and Active Measurement, PAM 2016, 2016, 42-54 p.Conference paper (Refereed)Text
Abstract [en]

Web trackers are services that monitor user behavior on the web. The information they collect is ostensibly used for customization and targeted advertising. Due to rising privacy concerns, users have started to install browser plugins that prevent tracking of their web usage. Such plugins tend to address tracking activity by means of crowdsourced filters. While these tools have been relatively effective in protecting users from privacy violations, their crowdsourced nature requires significant human effort, and provide no fundamental understanding of how trackers operate. In this paper, we leverage the insight that fundamental requirements for trackers’ success can be used as discriminating features for tracker detection. We begin by using traces from a mobile web proxy to model user browsing behavior as a graph. We then perform a transformation on the extracted graph that reveals very wellconnected communities of trackers. Next, after discovering that trackers’ position in the transformed graph significantly differentiates them from “normal” vertices, we design an automated tracker detection mechanism using two simple algorithms.We find that both techniques for automated tracker detection are quite accurate (over 97%) and robust (less than 2% false positives). In conjunction with previous research, our findings can be used to build robust, fully automated online privacy preservation systems.

Place, publisher, year, edition, pages
2016. 42-54 p.
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9631
Keyword [en]
Automation, Internet service providers, Online systems, Community structures, Detection mechanism, Fully automated, Online privacy, Privacy concerns, Privacy violation, Targeted advertising, User browsing behaviors, Behavioral research
National Category
Computer Systems
URN: urn:nbn:se:kth:diva-186743DOI: 10.1007/978-3-319-30505-9_4ScopusID: 2-s2.0-84962240808ISBN: 9783319305042OAI: diva2:931389
31 March 2016 through 1 April 2016

QC 20160527

Available from: 2016-05-27 Created: 2016-05-13 Last updated: 2016-05-27Bibliographically approved

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Kalavri, Vasiliki
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Software and Computer systems, SCS
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ReferencesLink to record
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