Detecting and defending against third-party tracking on the web 论文

摘要

While third-party tracking on the web has garnered much attention, its workings remain poorly understood. This poster describes our work from a recent paper [1], in which our goal is to dissect how mainstream web tracking occurs in the wild. We develop a client-side method for detecting and classifying five kinds of third-party trackers based on how they manipulate browser state. We run our detection system while browsing the web and observe a rich ecosystem, with over 500 unique trackers in our measurements alone. We find that most commercial pages are tracked by multiple parties, trackers vary widely in their coverage with a small number being widely deployed, and many trackers exhibit a combination of tracking behaviors. Based on web search traces taken from AOL data, we estimate that several trackers can each capture more than 20 % of a users browsing behavior. We further assess