CIF: Small: An Information Theoretic Framework for Web Privacy

互联网用户合理地希望他们的在线身份和网络浏览活动保持隐私。不幸的是，实际情况远非如此；事实上，用户一直在互联网上被跟踪。随着网络跟踪技术变得越来越复杂和普遍，迫切需要了解和量化网络用户的隐私风险，也就是说，互联网上的用户从他们的在线活动中被唯一识别的可能性有多大？这个项目从信息论的角度解决网络隐私问题。基于在线活动和社交网络连接的统计模型，该项目开发了一个统一的信息理论框架，以量化网络用户面临的在线攻击的隐私风险。这项拟议的研究通过三个相互关联的重点来解决互联网隐私问题，重点是指纹识别、社交网络去匿名化和协同攻击，并提供了一个用于试验真实网络的评估计划。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。

Internet users reasonably expect their online identities and web browsing activities to remain private. Unfortunately, this is far from the case in practice; in reality, users are constantly tracked on the internet. As web tracking technologies become more sophisticated and pervasive, there is a critical need to understand and quantify web users' privacy risk, that is, what is the likelihood that users on the internet can be uniquely identified from their online activities?This project addresses web privacy from an information theoretic perspective. Based on statistical models for online activity and social network connections, the project develops a unified information theoretic framework to quantify the privacy risk that web users face from online attacks. The proposed research addresses internet privacy through three interrelated thrusts focusing on fingerprinting, social network de-anonymization and synergistic attacks, and provides an evaluation plan to experiment with real-world networks.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.