CIF: Small: An Information Theoretic Framework for Web Privacy

CIF：小型：网络隐私的信息理论框架

• 批准号: 1815821
• 负责人: Elza Erkip
• 金额: $ 48.71万
• 依托单位: New York University
• 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=1815821&HistoricalAwards=false
• 关键词: CIF; Small; Information; Theoretic; Framework

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.

互联网用户合理地期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;在现实中，用户在互联网上不断被跟踪。随着网络跟踪技术变得越来越复杂和普遍，有一个关键的需要，以了解和量化网络用户的隐私风险，也就是说，什么是互联网上的用户可以从他们的在线活动唯一识别的可能性？这个项目从信息理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。该研究计划通过三个相互关联的方向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，并提供了一个评估计划，以在现实世界的网络中进行实验。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智力价值和更广泛的影响审查标准进行评估来支持。

项目成果

Fundamental Privacy Limits in Bipartite Networks Under Active Attacks

主动攻击下双向网络的基本隐私限制

• DOI: 10.1109/jsac.2022.3142299
• 发表时间: 2022
• 期刊: IEEE Journal on Selected Areas in Communications
• 影响因子: 16.4
• 作者: Shariatnasab, Mahshad;Shirani, Farhad;Erkip, Elza
• 通讯作者: Erkip, Elza

A Concentration of Measure Approach to Database De-anonymization

数据库去匿名化的集中测量方法

• DOI: 10.1109/isit.2019.8849392
• 发表时间: 2019
• 期刊: 2019 IEEE International Symposium on Information Theory (ISIT
• 作者: Shirani, Farhad;Garg, Siddharth;Erkip, Elza
• 通讯作者: Erkip, Elza

Database Matching Under Column Deletions

列删除下的数据库匹配

• DOI: 10.1109/isit45174.2021.9518145
• 发表时间: 2021
• 期刊: 2021 IEEE International Symposium on Information Theory (ISIT
• 作者: Bakirtas, Serhat;Erkip, Elza
• 通讯作者: Erkip, Elza

Matching Graphs with Community Structure: A Concentration of Measure Approach

将图与社区结构相匹配：集中测量方法

• DOI: 10.1109/allerton.2018.8636015
• 发表时间: 2018
• 期刊: and Computing (Allerton
• 作者: Shirani, Farhad;Garg, Siddharth;Erkip, Elza
• 通讯作者: Erkip, Elza

Distribution-Agnostic Database De-Anonymization Under Synchronization Errors

同步错误下与分布无关的数据库去匿名化

• DOI: 10.1109/wifs58808.2023.10374831
• 发表时间: 2023
• 期刊: IEEE WIFS 2023
• 作者: Bakirtas, Serhat;Erkip, Elza
• 通讯作者: Erkip, Elza