PURL: 安全高效的链接装饰清除方法
PURL: Safe and Effective Sanitization of Link Decoration
AI总结：
针对现有浏览器隐私防护无法抵御新型追踪技术的问题，提出基于网页执行跨层图表示的机器学习方法PURL，可安全高效清除链接装饰中的追踪信息，性能优于现有方案，且发现近四分之三的热门网站滥用链接装饰追踪用户。
摘要：
尽管主打隐私保护的浏览器已采取措施拦截第三方Cookie并缓解浏览器指纹追踪，但能够绕过现有防御机制的新型追踪技术仍在不断涌现。由于无论采用何种追踪技术，追踪器都需要通过链接装饰将信息从客户端传递到服务器端，因此一种颇具前景的互补方案是检测并清除带装饰的链接中的追踪信息。为此，我们提出PURL（发音为purel-l），这是一种机器学习方法，利用网页执行的跨层图表示来安全高效地清除链接装饰。我们的评估显示，PURL在准确率和减少网站功能损坏方面显著优于现有防御措施，同时对常见的规避技术具备鲁棒性。PURL在百万级热门网站样本上的部署表明，近四分之三的网站存在滥用链接装饰进行追踪的情况，通常被用于共享Cookie、电子邮件地址和指纹信息。
英文摘要：
While privacy-focused browsers have taken steps to block third-party cookies and mitigate browser fingerprinting, novel tracking techniques that can bypass existing countermeasures continue to emerge. Since trackers need to share information from the client-side to the server-side through link decoration regardless of the tracking technique they employ, a promising orthogonal approach is to detect and sanitize tracking information in decorated links. To this end, we present PURL (pronounced purel-l), a machine-learning approach that leverages a cross-layer graph representation of webpage execution to safely and effectively sanitize link decoration. Our evaluation shows that PURL significantly outperforms existing countermeasures in terms of accuracy and reducing website breakage while being robust to common evasion techniques. PURL's deployment on a sample of top-million websites shows that link decoration is abused for tracking on nearly three-quarters of the websites, often to share cookies, email addresses, and fingerprinting information.
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。
