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.

互联网测量数据在实现网络和网络安全监控等领域的创新方面发挥着关键作用，然而，此类数据通常包含敏感的用户信息，这些信息无法公开共享，并且只能由有限数量的已建立合作关系的可信研究人员访问。这种限制阻碍了研究结果的可重复性以及研究社区对收集数据的使用，该项目开发了一种促进弗吉尼亚大学 (UVA) 和弗吉尼亚理工大学 (VT) 系统校园网络流量日志数据共享的方法。 ）与更广泛的研究界。该项目的新颖之处在于（i）数据匿名化：自动发现和匿名化网络流量日志中的敏感数据，以及（ii）数据共享：通过隐私保护框架模块化数据访问该项目的更广泛意义和重要性是（i）。扩大可以使用 UVA 和 VT 校园网络流量日志的研究人员数量，并作为其他校园和网络共享互联网测量数据的指针 (ii) 课程开发，吸引研究生和本科生参与研究活动；具体来说，该项目（i）开发了一种可扩展且适应性强的模型来识别个人身份信息（PII），以在离线环境中对结构化和半结构化网络日志进行匿名化，并根据历史日志对模型进行微调。 ，并将模型转换为能够支持实时数据源和离线存储库的可调整服务；（ii）根据研究需求模块化不同级别的数据访问，并构建具有差异隐私的隐私保护框架以提供可证明的数据访问级别的隐私保证；(iii) 将隐私保护框架集成到数据匿名化中，以实现基于使用模式的动态匿名化。该项目直接在 UVA 和 VT 限制流量收集管道上部署和评估所提出的方法。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。