阻塞或中断？自动检测隐私干预何时破坏网站
Blocked or Broken? Automatically Detecting When Privacy Interventions Break Websites
摘要：
开发和维护众包过滤器列表的一个核心问题是，它们的维护者无法自信地预测新的过滤器列表规则是否（以及在何处）会破坏网站。这是由于Web的不可分割性，它阻止了过滤器列表作者在向数百万用户发布新的阻止规则之前广泛了解其影响。过滤器列表作者无法在发布新规则之前评估其对Web兼容性的影响，这严重降低了基于过滤器列表的内容阻止的好处：过滤器列表既过于保守（即规则被狭隘地定制以减少破坏的风险）又容易出错（即阻止工具仍然会破坏大量网站）。为了扩展到Web的大小和范围，过滤器列表作者需要一个自动化系统来检测新的过滤规则何时破坏网站，在这种破坏有机会到达最终用户之前。在这项工作中，我们设计并实现了第一个自动化系统，用于预测过滤器列表规则何时会破坏网站。我们构建了一个分类器，在EasyList项目和新型浏览器工具的兼容性数据组合生成的数据集上进行训练，并发现它准确到实用水平（AUC 0.88）。我们的开源系统在评估拟议的隐私干预的兼容性风险时不需要人工交互。我们还提出了40个页面行为，最预测破损观察网站。
英文摘要：
A core problem in the development and maintenance of crowd-sourced filter lists is that their maintainers cannot confidently predict whether (and where) a new filter list rule will break websites. This is a result of enormity of the Web, which prevents filter list authors from broadly understanding the impact of a new blocking rule before they ship it to millions of users. The inability of filter list authors to evaluate the Web compatibility impact of a new rule before shipping it severely reduces the benefits of filter-list-based content blocking: filter lists are both overly-conservative (i.e. rules are tailored narrowly to reduce the risk of breaking things) and error-prone (i.e. blocking tools still break large numbers of sites). To scale to the size and scope of the Web, filter list authors need an automated system to detect when a new filter rule breaks websites, before that breakage has a chance to make it to end users. In this work, we design and implement the first automated system for predicting when a filter list rule breaks a website. We build a classifier, trained on a dataset generated by a combination of compatibility data from the EasyList project and novel browser instrumentation, and find it is accurate to practical levels (AUC 0.88). Our open source system requires no human interaction when assessing the compatibility risk of a proposed privacy intervention. We also present the 40 page behaviors that most predict breakage in observed websites.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
