Like a Pack of Wolves: Community Structure of Web Trackers

Abstract

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 well-connected 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.