[论文解读] Who Filters the Filters: Understanding the Growth, Usefulness and Efficiency of Crowdsourced Ad Blocking

Peter J. Snyder, Antoine Vastel+1 more|arXiv (Cornell University)|Oct 22, 2018

Privacy, Security, and Data Protection被引用 5

一句话总结

本文分析了最受欢迎的众包广告过滤规则列表EasyList，发现其90.16%的规则在常见浏览场景中毫无益处。通过测量规则应用率并识别规避策略，作者提出了优化的过滤策略，使桌面性能提升62.5%，并实现移动设备上的高效部署，同时保持超过99%的阻断覆盖率。

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.

研究动机与目标

• 理解众包过滤列表（如EasyList）的长期增长、实用性及性能成本。
• 确定EasyList中在真实浏览场景下无效或‘无用负担’的规则比例。
• 分析广告商通过URL和结构变化规避检测的手段。
• 开发可降低资源开销而不牺牲阻断效果的优化过滤策略。
• 实现在性能受限的移动设备上高效部署过滤列表。

提出的方法

• 在超过两个月的时间内，对10,000个网站样本（5,000个热门网站和5,000个长尾网站）应用EasyList的每日更新。
• 测量规则应用率，以确定哪些规则在真实浏览环境中被实际使用。
• 通过分析数据集中URL和请求模式的变化，对广告商的规避技术进行分类。
• 提出一种两阶段过滤策略：基于规则效用的预过滤，以及动态规则评估，以减少运行时开销。
• 使用桌面和类移动环境中的真实世界基准测试，评估性能改进效果。
• 利用EasyList长达9年的纵向数据，建模规则生命周期、插入与删除模式。

实验结果

研究问题

• RQ1 以规则数量随时间的变化衡量，EasyList的增长率是多少？
• RQ2 EasyList中活跃规则数量和规则匹配次数随时间如何变化？
• RQ3 规则的效用是否会随时间下降？在常见浏览场景中，有多少比例的规则是有用的？
• RQ4 网站是否会尝试规避新的广告过滤规则？如果是，主要的规避策略是什么？
• RQ5 过时的过滤规则对主流广告过滤工具的性能成本有多大？

主要发现

• EasyList中90.16%的资源阻断规则在常见浏览场景中无任何益处，表明规则膨胀现象普遍存在。
• 新规则的添加频率比旧规则的删除频率高出1.7倍，导致大量无效或过时规则持续累积。
• 广告商频繁通过更改URL和结构来规避检测，其中8.7%的热门网站在某个时间点使用过反广告屏蔽脚本。
• 一种新型两阶段过滤策略使桌面端阻断性能提升62.5%，同时保持EasyList超过99%的阻断覆盖率。
• 该优化策略通过减少规则评估开销，使EasyList能在资源受限的移动设备上高效应用。
• 本研究揭示，过滤列表存在显著的技术债务，其性能成本的增长速度远超其实际效用。