SaTC: CORE: Small: Models and Measurements for Website Fingerprinting

个人与其朋友、家人、雇主和机构之间的许多私人互动现在都是在互联网上进行的；披露这些互动的内容，甚至这些各方之间的仅仅是关联，可能会使人们面临真正的财务或身体风险。因此，加密和隐藏用户与他们访问的网站之间的连接的虚拟专用网络或Tor项目等服务越来越受欢迎。网站指纹攻击使用这些技术没有隐藏的信息，如文件大小和下载时间，来重新识别用户访问的网站，但尽管这些攻击在实验室环境中有效，但在实际环境中评估它们并制定有效的防护措施是一个挑战。这个项目将应用统计学和机器学习来开发新的网站“指纹”的概率模型，这些模型揭示的信息量的度量，以及隐私保护算法和数据集来测试这些模型。这些模型和测试的结果将被用来评估网站指纹构成的威胁，并为新防御机制的设计提供信息。因此，用户将受益于改进的保护技术，而其他研究人员可以使用产生的模型、数据集和指标来研究网站指纹防御的有效性。这项工作和数据还将通过课程和研究培训来支持本科生和研究生教育。该项目将寻求解决网站指纹研究中的三个关键挑战--隐私保护背景流量的表征，维护指纹数据库，以及防御措施的评估和比较-通过开发新的网站指纹表示，可以为特定类型的下载生成的任何指纹赋予可能性。使用这些表示法，该项目将追求四个主要推动力。首先，该项目将使用这些模型来确定网站指纹可以在多大程度上直接推断下载的识别特征，而不需要所有可能的网页的数据库，从现有的封闭世界方法到网站指纹识别方法，再到更广泛适用的开放世界方法。其次，该项目将开发算法，在使用差异隐私概念保护个人用户隐私的同时，对实时流量模型进行培训。第三，使用经过训练的模型，该项目将提供对真实数据的攻击和防御的第一次评估，并使用信息论的指标在平等的基础上比较这些攻击和防御。最后，该项目将利用这些评估的结果来开发新的防御技术，这些技术可以直接应用于隐私敏感网站的内容，以及旨在保护用户下载的系统，如Tor网络。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Many private interactions between individuals and their friends, families, employers, and institutions are now carried out on the Internet; disclosure of the contents of these interactions or even the mere associations between these parties can expose people to real financial or physical risks. As a result, encryption and services such as virtual private networks or the Tor project that conceal the connection between a user and the websites they visit are growing in popularity. Website fingerprinting attacks use information that is not concealed by these techniques, such as file sizes and download times, to re-identify the websites a user visits, but while these attacks work in a lab environment, it is a challenge to evaluate them in practical settings and develop effective protections against them. This project will apply statistics and machine learning to develop new probabilistic models of the "fingerprint" of a website, metrics of the amount of information these models reveal, and privacy preserving algorithms and datasets to test these models. The results of these models and tests will be used to assess the threat posed by website fingerprinting and inform the design of new defense mechanisms. As a result, users will benefit from improved protection techniques, while other researchers can use the resulting models, datasets, and metrics to study the effectiveness of website fingerprinting defenses. The work and data will also be used to support both undergraduate and graduate education through both courses and research training.This project will seek to address three key challenges in website fingerprinting research -- privacy-preserving characterization of background traffic, maintaining fingerprint databases, and evaluation and comparison of defenses -- by developing new representations of website fingerprints that can assign a likelihood to any fingerprint being generated by a specific type of download. Using these representations, the project will pursue four main thrusts. First, the project will use these models to determine the extent to which website fingerprints can directly infer identifying features of a download without requiring a database of all possible web pages, moving from existing closed world approaches to website fingerprinting toward more broadly applicable open world approaches. Second, the project will develop algorithms to train the models on live traffic while preserving the privacy of individual users using concepts from differential privacy. Third, using the trained models, the project will provide the first assessment of attacks and defenses on realistic data, and use metrics from information theory to compare those attacks and defenses on an equal footing. Finally, the project will use the results of these evaluations to develop new defensive techniques that can be applied directly to the content of privacy-sensitive sites and to systems designed to protect users' downloads such as the Tor network.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.