Who Filters the Filters: Understanding the Growth, Usefulness and Efficiency of Crowdsourced Ad Blocking

Ad and tracking blocking extensions are popular tools for improving web\nperformance, privacy and aesthetics. Content blocking extensions typically rely\non filter lists to decide whether a web request is associated with tracking or\nadvertising, and so should be blocked. Millions of web users rely on filter\nlists to protect their privacy and improve their browsing experience.\n Despite their importance, the growth and health of filter lists are poorly\nunderstood. Filter lists are maintained by a small number of contributors, who\nuse a variety of undocumented heuristics to determine what rules should be\nincluded. Lists quickly accumulate rules, and rules are rarely removed. As a\nresult, users' browsing experiences are degraded as the number of stale, dead\nor otherwise not useful rules increasingly dwarf the number of useful rules,\nwith no attenuating benefit. An accumulation of "dead weight" rules also makes\nit difficult to apply filter lists on resource-limited mobile devices.\n This paper improves the understanding of crowdsourced filter lists by\nstudying EasyList, the most popular filter list. We find that EasyList has\ngrown from several hundred rules, to well over 60,000 rules, during its 9-year\nhistory. We measure how EasyList affects web browsing by applying EasyList to a\nsample of 10,000 websites. We find that 90.16% of the resource blocking rules\nin EasyList provide no benefit to users in common browsing scenarios. We\nfurther use our changes in EasyList application rates to provide a taxonomy of\nthe ways advertisers evade EasyList rules. Finally, we propose optimizations\nfor popular ad-blocking tools, that allow EasyList to be applied on performance\nconstrained mobile devices, and improve desktop performance by 62.5%, while\npreserving over 99% of blocking coverage.\n