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

TL;DR

This paper analyzes the growth, effectiveness, and efficiency of crowdsourced ad blocking filter lists, revealing that most rules are ineffective and proposing optimizations to improve performance especially on mobile devices.

Contribution

It provides a detailed study of EasyList's growth and effectiveness, introduces a taxonomy of ad evasion techniques, and proposes optimizations for better performance on resource-limited devices.

Findings

90.16% of EasyList rules provide no benefit in common browsing scenarios

EasyList has grown from hundreds to over 60,000 rules over 9 years

Proposed optimizations improve desktop performance by 62.5% while maintaining over 99% blocking coverage

Abstract

Ad and tracking blocking extensions are popular tools for improving web performance, privacy and aesthetics. Content blocking extensions typically 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 a variety of undocumented heuristics 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 find that EasyList has grown from several hundred rules, to well over 60,000 rules, during its 9-year history. We measure how EasyList affects web browsing by applying EasyList to a sample 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 further use our changes in EasyList application rates to provide a taxonomy of the ways advertisers evade EasyList rules. Finally, we propose optimizations for popular ad-blocking tools, that allow EasyList to be applied on performance constrained mobile devices, and improve desktop performance by 62.5%, while preserving over 99% of blocking coverage.