Collaborative Research: SaTC: CORE: Medium: Defending against Emerging Stateless Web Tracking

协作研究：SaTC：核心：中：防御新兴的无状态网络跟踪

基本信息

批准号：
2138139
负责人：
Zubair Shafiq
金额：
$ 40万
依托单位：
University of California-Davis
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-15 至 2026-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2138139&HistoricalAwards=false
关键词：
Collaborative Research SaTC CORE Medium

项目摘要

The web is continuously evolving to support new features and accommodate the constantly-changing demands of users and web applications. At the same time, the online behavioral advertising ecosystem exploits these emerging features for privacy-invasive tracking of web users. Traditionally, tracking on the web has been abusing browser features such as cookies, but new advanced web tracking techniques (browser fingerprinting) are emerging. Browser fingerprinting techniques capitalize on users having subtle differences in their browser configurations that make them identifiable. The project’s novelties are to advance the current state of knowledge of web tracking and how we can defend against it. The project's broader significance and importance are to enhance the privacy of millions of users by improving countermeasures against web tracking that can be deployed in mainstream and privacy-focused web browsers. The main goal of this project is to fundamentally change the detection and defense against stateless web tracking. Unlike prior work that relies on manual or ad-hoc approaches, this project takes a principled approach that leverages browser instrumentation to build signatures of usage of web APIs, the building blocks of browser fingerprinting. The project builds machine learning techniques to improve stateless tracking detection and to quantify the privacy risks of different web APIs. Furthermore, the state-of-the-art approaches to counter stateless tracking are ineffective and brittle in the face of adversarial tactics and also degrade user experience due to their coarse-grained and opaque nature. This project mitigates fingerprinting behavior with fine-grained techniques and infers the intent behind the usage of web APIs, thus enforcing the right mitigation at the right time to minimize the breakage of legitimate functionality. The project also makes the machine learning decisions explainable to better inform users and browser vendors of the progress of stateless tracking on the web.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.

网络不断支持新功能并适应用户和网络应用程序不断变化的需求，同时，在线广告生态系统利用这些新兴功能对不断变化的网络用户进行侵犯隐私的跟踪。一直在滥用 cookie 等浏览器功能，但新的高级网络跟踪技术（浏览器指纹识别）正在出现。浏览器指纹识别技术利用了用户浏览器配置的细微差异，使他们能够被识别。该项目的新颖之处在于推进了当前的知识水平。该项目的更广泛意义和重要性是通过改进可部署在主流和注重隐私的网络浏览器中的网络跟踪对策来增强数百万用户的隐私。该项目旨在从根本上改变对无状态 Web 跟踪的检测和防御，与依赖手动或临时方法的先前工作不同，该项目采用一种原则性方法，利用浏览器工具来构建 Web API（Web API 的构建块）的使用签名。浏览器指纹识别。构建机器学习技术来改进无状态跟踪检测并量化不同 Web API 的隐私风险。此外，对抗无状态跟踪的最先进方法在面对对抗策略时是无效且脆弱的，还会降低用户体验。由于其粗粒度和不透明的性质，该项目通过细粒度技术来减轻指纹识别行为，并推断出 Web API 使用背后的意图，从而在正确的时间实施正确的缓解措施，以最大程度地减少破坏。该项目还使机器学习决策变得可解释，以更好地告知用户和浏览器供应商网络上无状态跟踪的进展。该奖项反映了法定使命，并通过使用基金会的智力价值和评估进行评估，被认为值得支持。更广泛的影响审查标准。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

AutoFR: Automated Filter Rule Generation for Adblocking

AutoFR：自动生成广告拦截过滤规则

DOI：
发表时间：
2023-01
期刊：
USENIX Security Symposium
影响因子：
0
作者：
Hieu Le;Salma Elmalaki;Athina Markopoulou;Zubair Shafiq
通讯作者：
Zubair Shafiq

CookieGraph: Understanding and Detecting First-Party Tracking Cookies

CookieGraph：理解和检测第一方跟踪 Cookie

DOI：
发表时间：
2023-01
期刊：
ACM Conference on Computer and Communications Security (CCS
影响因子：
0
作者：
Shaoor Munir;Sandra Siby;Umar Iqbal;Steven Englehardt;Zubair Shafiq;Carmela Troncoso
通讯作者：
Carmela Troncoso

Blocking JavaScript without Breaking the Web

在不破坏网络的情况下阻止 JavaScript