Errors, Misunderstandings, and Attacks: Analyzing the Crowdsourcing Process of Ad-blocking Systems

Abstract

Ad-blocking systems such as Adblock Plus rely on crowdsourcing to build and maintain filter lists, which are the basis for determining which ads to block on web pages. In this work, we seek to advance our understanding of the ad-blocking community as well as the errors and pitfalls of the crowdsourcing process. To do so, we collected and analyzed a longitudinal dataset that covered the dynamic changes of popular filter-list EasyList for nine years and the error reports submitted by the crowd in the same period. Our study yielded a number of significant findings regarding the characteristics of FP and FN errors and their causes. For instances, we found that false positive errors (i.e., incorrectly blocking legitimate content) still took a long time before they could be discovered (50% of them took more than a month) despite the community effort. Both EasyList editors and website owners were to blame for the false positives. In addition, we found that a great number of false negative errors (i.e., failing to block real advertisements) were either incorrectly reported or simply ignored by the editors. Furthermore, we analyzed evasion attacks from ad publishers against ad-blockers. In total, our analysis covers 15 types of attack methods including 8 methods that have not been studied by the research community. We show how ad publishers have utilized them to circumvent ad-blockers and empirically measure the reactions of ad blockers. Through in-depth analysis, our findings are expected to help shed light on any future work to evolve ad blocking and optimize crowdsourcing mechanisms.