Who Filters the Filters
Who Filters the Filters
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谁过滤过滤器
DOI：
10.1145/3392144
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
B. Livshits
中科院分区：
文献类型：
--
作者：
Antoine Vastel;Peter Snyder;B. Livshits
广告和跟踪阻止扩展是提高web性能，隐私和美观的流行工具。内容拦截扩展通常依赖于过滤列表来决定一个web请求是否与跟踪或广告有关，因此应该被阻止。数以百万计的网络用户依靠过滤列表来保护他们的隐私并改善他们的浏览体验。尽管它们很重要，但人们对过滤列表的发展和健康却知之甚少。过滤器列表由少数贡献者维护，他们使用未记录的启发式和直觉来确定应该包含哪些规则。列表快速积累规则，而规则很少被删除。因此，用户的浏览体验会下降，因为过时、死亡或无用的规则数量越来越多地使有用规则的数量相形见绌，而没有减弱的好处。“累赘”规则的积累也使得在资源有限的移动设备上应用过滤列表变得困难。本文通过对最流行的过滤列表EasyList的研究，提高了对众包过滤列表的理解。我们通过将EasyList应用于10,000个网站的样本来衡量EasyList对网页浏览的影响。我们发现，在常见的浏览场景下，EasyList中90.16%的资源阻断规则对用户没有任何好处。我们使用对规则应用率的测量来对广告商逃避EasyList规则的方式进行分类。最后，我们对流行的广告拦截工具提出了优化建议，(i)允许EasyList应用于性能受限的移动设备，（ii）将桌面性能提高62.5%，同时保留99%以上的拦截覆盖率。我们希望这些优化对非英语本地用户最有用，他们依赖于补充过滤列表来进行有效的阻止和保护。
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.
DOI：
10.14722/ndss.2018.23331
发表时间：
2018
期刊：
--
影响因子：
--
作者：
Shitong Zhu;Xunchao Hu;Zhiyun Qian;Zubair Shafiq;Heng Yin
通讯作者：
Shitong Zhu;Xunchao Hu;Zhiyun Qian;Zubair Shafiq;Heng Yin
错误、误解和攻击：分析广告拦截系统的众包流程
DOI：
10.1145/3355369.3355588
发表时间：
2019
期刊：
ACM SIGCOMM Internet Measurement Conference (IMC
影响因子：
--
作者：
Alrizah, Mshabab;Zhu, Sencun;Xing, Xinyu;Wang, Gang
通讯作者：
Wang, Gang