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 performance, privacy and aesthetics. Content blocking extensions generally rely on filter lists to decide whether a web request is associated with tracking or advertising, and so should be blocked. Millions of web users rely on filter lists to protect their privacy and improve their browsing experience. Despite their importance, the growth and health of filter lists are poorly understood. Filter lists are maintained by a small number of contributors who use undocumented heuristics and intuitions to determine what rules should be included. Lists quickly accumulate rules, and rules are rarely removed. As a result, users' browsing experiences are degraded as the number of stale, dead or otherwise not useful rules increasingly dwarf the number of useful rules, with no attenuating benefit. An accumulation of "dead weight" rules also makes it difficult to apply filter lists on resource-limited mobile devices. This paper improves the understanding of crowdsourced filter lists by studying EasyList, the most popular filter list. We measure how EasyList affects web browsing by applying EasyList to a sam- ple of 10,000 websites. We find that 90.16% of the resource blocking rules in EasyList provide no benefit to users in common browsing scenarios. We use our measurements of rule application rates to taxonomies ways advertisers evade EasyList rules. Finally, we propose optimizations for popular ad-blocking tools that (i) allow EasyList to be applied on performance constrained mobile devices and (ii) improve desktop performance by 62.5%, while preserving over 99% of blocking coverage. We expect these optimizations to be most useful for users in non-English locals, who rely on supplemental filter lists for effective blocking and protections.