SaTC: Frontiers: Collaborative: Protecting Personal Data Flow on the Internet

SaTC：前沿：协作：保护互联网上的个人数据流

基本信息

批准号：
2103439
负责人：
Zubair Shafiq
金额：
$ 110万
依托单位：
University of California-Davis
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2025-09-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2103439&HistoricalAwards=false
关键词：
SaTC Frontiers Collaborative Protecting Personal

项目摘要

Personal data collection typically starts on user devices, with the data then shared with service providers and trackers, obtained by malicious actors, and/or used for surveillance. The services enabled by this data come at the expense of privacy, security, transparency, and fairness, for individuals and society as a whole. Increased public awareness has led to landmark legislation on data protection, such as the EU’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Policymakers need to be further informed by technology, however, to formulate relevant and enforceable policies, and end-users still need tools to protect themselves. This project seeks to protect personal information, by improving the transparency and control of data flow on the Internet, using a multidisciplinary approach that combines methodologies from computer science (theory, network measurement, security) with policy and economics, and crosses multiple application domains (web, mobile, and Internet-of-Things). Conceptual frameworks are developed for personal information flow on the Internet, as well as systems for monitoring and mediation. Existing systems are improved for measuring the tracking and discrimination of personal information, and for explicitly controlling privacy-utility tradeoffs. To provide long-term privacy-by-design alternatives, the project pursues verifiable IoT architectures seeking to decentralize the advertising ecosystem and eliminate intermediaries. The project likewise leverages technology to inform policy specification and to provide tools to audit and enforce policies. The broader impacts of the project include: (1) informing policymakers, nonprofit advocates, and industry players through interactions with relevant stakeholders; (2) training next-generation graduate and undergraduate students jointly in technology and policy; and (3) broadening participation of women, underrepresented minorities, and community college students.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.

个人数据收集通常从用户设备开始，然后与服务提供商和跟踪器共享数据，由恶意行为者获取和/或用于监视，而这些数据启用的服务是以牺牲隐私、安全性、透明度和隐私为代价的。对于个人和整个社会而言，公众意识的提高导致了具有里程碑意义的数据保护立法，例如欧盟的《通用数据保护条例》（GDPR）和《加州消费者隐私法》（CCPA），政策制定者需要进一步了解。然而，制定相关且可执行的技术该项目旨在通过提高互联网数据流的透明度和控制，使用结合了计算机科学方法（理论、网络测量、安全）与政策和经济，并跨越多个应用领域（网络、移动和物联网）。为互联网上的个人信息流开发了概念框架，并对现有系统进行了改进。用于测量跟踪和歧视为了提供长期的隐私设计替代方案，该项目追求可验证的物联网架构，旨在分散广告生态系统并消除中介机构。该项目的更广泛影响包括：（1）通过与相关利益相关者的互动向政策制定者、非营利倡导者和行业参与者提供信息；（2）联合培训下一代研究生和本科生。和政策；以及(3) 扩大女性、代表性不足的少数族裔和社区学院学生的参与。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，认为值得支持。

项目成果

期刊论文数量（14）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Harpo: Learning to Subvert Online Behavioral Advertising

Harpo：学习颠覆在线行为广告

DOI：
发表时间：
2022-01
期刊：
Network and Distributed System Security Symposium (NDSS
影响因子：
0
作者：
Jiang Zhang;Konstantinos Psounis;Muhammad Haroon;Zubair Shafiq
通讯作者：
Zubair Shafiq

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

Fingerprinting the Fingerprinters: Learning to Detect Browser Fingerprinting Behaviors

对指纹识别器进行指纹识别：学习检测浏览器指纹识别行为

DOI：
10.1109/sp40001.2021.00017
发表时间：
2020-08-11
期刊：
2021 IEEE Symposium on Security and Privacy (SP)
影响因子：
0
作者：
Umar Iqbal;Steven Englehardt;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

DNN Model Architecture Fingerprinting Attack on CPU-GPU Edge Devices

CPU-GPU 边缘设备上的 DNN 模型架构指纹攻击