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

SaTC：核心：小型：网站指纹识别的模型和测量

• 批准号: 1815757
• 负责人: Nicholas Hopper
• 金额: $ 49.97万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1815757&HistoricalAwards=false
• 关键词: SaTC; CORE; Small; Models; Measurements

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.

个人与朋友、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，甚至只是这些当事人之间的联系，都可能使人们面临真实的财务或身体风险。因此，加密和服务，如虚拟专用网络或Tor项目，隐藏用户和他们访问的网站之间的连接越来越受欢迎。 网站指纹攻击使用这些技术无法隐藏的信息（例如文件大小和下载时间）来重新识别用户访问的网站，但是虽然这些攻击在实验室环境中工作，但在实际环境中评估它们并制定有效的保护措施是一项挑战。 该项目将应用统计学和机器学习来开发网站“指纹”的新概率模型，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用于评估网站指纹识别带来的威胁，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由此产生的模型，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用于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--保护背景流量的隐私特征，维护指纹数据库，评估和比较防御措施--通过开发网站指纹的新表示，其可以将可能性分配给由特定类型的下载生成的任何指纹。 利用这些表现形式，该项目将追求四个主要目标。 首先，该项目将使用这些模型来确定网站指纹在多大程度上可以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转向更广泛适用的开放世界方法。 其次，该项目将开发算法，在实时流量上训练模型，同时使用差分隐私的概念保护个人用户的隐私。 第三，使用经过训练的模型，该项目将提供对现实数据的攻击和防御的第一次评估，并使用信息理论的指标来平等地比较这些攻击和防御。 最后，该项目将利用这些评估的结果开发新的防御技术，这些技术可以直接应用于隐私敏感网站的内容和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，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

p1-FP: Extraction, Classification, and Prediction of Website Fingerprints with Deep Learning

p1-FP：利用深度学习提取、分类和预测网站指纹

• DOI: 10.2478/popets-2019-0043
• 发表时间: 2019
• 期刊: Proceedings on Privacy Enhancing Technologies
• 作者: Oh, Se Eun;Sunkam, Saikrishna;Hopper, Nicholas
• 通讯作者: Hopper, Nicholas

SoK: A Critical Evaluation of Efficient Website Fingerprinting Defenses

• DOI: 10.1109/sp46215.2023.10179289
• 发表时间: 2023-05
• 期刊: 2023 IEEE Symposium on Security and Privacy (SP)
• 作者: Nate Mathews;James K. Holland;Se Eun Oh;Mohammad Saidur Rahman;Nicholas Hopper;M. Wright

Padding-only Defenses Add Delay in Tor

仅填充防御会增加 Tor 的延迟

• DOI: 10.1145/3559613.3563207
• 发表时间: 2022
• 期刊: WPES'22: Proceedings of the 21st Workshop on Privacy in the Electronic Society
• 作者: Witwer, Ethan;Holland, James K.;Hopper, Nicholas
• 通讯作者: Hopper, Nicholas

DeepCoFFEA: Improved Flow Correlation Attacks on Tor via Metric Learning and Amplification

DeepCoFFEA：通过度量学习和放大改进对 Tor 的流相关攻击

• DOI: 10.1109/sp46214.2022.9833801
• 发表时间: 2022
• 期刊: IEEE Symposium on Security and Privacy (SP
• 作者: Oh, Se Eun;Yang, Taiji;Mathews, Nate;Holland, James K;Rahman, Mohammad Saidur;Hopper, Nicholas;Wright, Matthew
• 通讯作者: Wright, Matthew

RegulaTor: A Straightforward Website Fingerprinting Defense

Regulator：简单的网站指纹防御

• DOI: 10.2478/popets-2022-0049
• 发表时间: 2022
• 期刊: Proceedings on Privacy Enhancing Technologies
• 作者: Holland, James K;Hopper, Nicholas
• 通讯作者: Hopper, Nicholas