Collaborative Research: IMR: MM-1B: Automating Privacy-Preserving Data Sharing of Campus Network Traffic Logs

合作研究：IMR：MM-1B：自动化校园网络流量日志的隐私保护数据共享

• 批准号: 2319422
• 负责人: Mark Gardner
• 金额: $ 5万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2319422&HistoricalAwards=false
• 关键词: Collaborative; Research; IMR; MM; 1B

Internet measurement data serves a critical role in enabling innovations in fields such as networking and cyber security monitoring. However, such data often contains sensitive user information, which cannot be shared publicly and is only accessible to a limited number of trusted researchers with established collaborations with data owners. This limitation hinders the reproducibility of research results and the use of collected data by the research community. This project develops a systematic approach to facilitate data sharing of campus network traffic logs from the University of Virginia (UVA) and Virginia Tech (VT) with the broader research community. The project’s novelties are (i) data anonymization: automating the discovery and anonymization of sensitive data in network traffic logs and (ii) data sharing: modularizing the data access through a privacy-preserving framework. The project's broader significance and importance are (i) expanding the number of researchers who can use the campus network traffic logs from UVA and VT, as well as serving as a pointer for sharing Internet measurement data by other campuses and networks; (ii) curriculum development to engage graduate and undergraduate students for research activities on the campus network logs. Specifically, the project (i) develops an extensible and adaptable model to identify Personal Identifiable Information (PII) to anonymize in structured and semi-structured network logs in an offline setting, fine tune the model based on historical logs, and transition the model to a tunable service capable of supporting live data feeds and offline repositories; (ii) modularizes varying levels of data access based on research needs and constructs a privacy-preserving framework with differential privacy to provide provable privacy guarantees to restricted levels of data access; and (iii) integrates the privacy-preserving framework back into data anonymization to achieve dynamic anonymization based on usage patterns. This project deploys and evaluates the proposed approach directly on the UVA and VT traffic collection pipeline in a practical real-world setting.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.

互联网测量数据在启用网络和网络安全监控等领域的创新方面起着关键作用。但是，这样的数据通常包含敏感用户信息，这些信息不能公开共享，只有有限数量的受信任的研究人员可以与数据所有者进行合作。这种限制阻碍了研究结果的可重复性以及研究界收集的数据的使用。该项目开发了一种系统的方法来促进弗吉尼亚大学（UVA）和弗吉尼亚理工大学（VT）与更广泛的研究社区的校园网络交通日志的数据共享。项目的新颖性是（i）数据匿名：自动化网络流量日志中敏感数据的发现和匿名化和（ii）数据共享：通过隐私保护框架模块化数据访问。该项目的重要性和重要性是（i）扩大可以使用UVA和VT校园网络流量日志的研究人员的数量，并用作用于与其他校园和网络共享互联网测量数据的指针； （ii）课程开发与研究生和本科生一起在校园网络日志上进行研究活动。具体而言，该项目（i）开发了一个可扩展且可自适应的模型，以识别个人可识别信息（PII），以在离线设置中的结构化和半结构化网络日志中匿名化，根据历史日志对模型进行微调，并将模型转换为可调的可调服务，能够支持实时数据和离线存储仪； （ii）根据研究需求将不同级别的数据访问模块化，并构建具有不同隐私的隐私保护框架，以提供可证明的隐私保证，以确保限制数据访问级别； （iii）将隐私保护框架重新集成到数据匿名化中，以基于用法模式实现动态匿名化。该项目在实用现实世界中直接在UVA和VT交通收集管道上进行了部署并评估所提出的方法。该奖项反映了NSF的法定任务，并通过评估基金会的知识分子优点和更广泛的影响来审查标准。