Collaborative Research: IMR:MM-1B: Privacy in Internet Measurements Applied To WAN and Telematics

合作研究：IMR:MM-1B：应用于广域网和远程信息处理的互联网测量隐私

• 批准号: 2319410
• 负责人: Christos Papadopoulos
• 金额: $ 22.01万
• 依托单位: University of Memphis
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2319410&HistoricalAwards=false
• 关键词: Collaborative; Research; IMR; MM; 1B

The PIMAWAT (Privacy in Internet Measurements Applied to WAN And Telematics) project will demonstrate new methods to provide data networking datasets that respect end-user privacy, while still being able to support new research in network protocols, security, privacy, and machine learning. The main insight is that *most data today sent over the wide-area network (WAN) is encrypted*; thus, the challenge is to demonstrate what data is encrypted, detect and scrub any remaining leaks, and finally anonymize the metadata (who talks to whom) before sharing data.The intellectual merit of PIMAWAT will be to develop new methods to anonymize network traffic at scale, then use those new algorithms to evaluate potential data leakage, and demonstrate that real-world data sources can be scrubbed for sharing while respecting privacy. PIMAWAT plans to focus the investigator’s prior work on wide-area network data traffic. As possible, it will also explore vehicle telematics as a recently developing dataset that poses unique privacy opportunities and challenges, with a device (not person) focus, yet with geolocation and application details.The broader impacts of PIMAWAT will be to democratize the potential to collect and share network data through new tools and best-practices for privacy-respecting data scrubbing. Data from this project will enable new approaches in computer science for protocol design and cyber-security, applying AI and machine learning, and will provide early results in the rapidly evolving field of vehicle telematics. PIMAWAT will provide new tools, data, and practices, and encourage use of these methods by other researchers, in classrooms, and by industry.The PIMAWAT project website will be https://ant.isi.edu/pimawat and its tools and datasets will be provided through https://ant.isi.edu/datasets/ as they are developed during project and after it completes.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.

Pimawat（应用于WAN和远程信息处理的Internet测量中的隐私）项目将展示新方法，以提供尊重最终用户隐私的数据网络数据集，同时仍然能够支持网络协议，安全性，隐私，机器学习中的新研究。主要见解是 *当今通过广阔网络（WAN）发送的大多数数据是加密的 *；因此，挑战是要证明哪些数据是加密的，检测和擦洗任何剩余的泄漏，最后将元数据（与谁交谈）匿名化。Pimawat的智力优点是开发新方法来匿名网络流量，然后使用这些新的算法来评估潜在的数据泄漏，以评估潜在的数据泄漏，以表现出真正的数据泄漏，并可以涉及真正的数据搜索，并且可以涉及搜索现实的数据。 Pimawat计划将研究者的先前工作重点放在广阔的网络数据流量上。它还将作为最近开发的数据集探索车辆远程信息处理，它带来了独特的隐私机会和挑战，设备（而不是人）的重点，但使用地理位置和应用细节。Pimawat的更广泛的影响是民主化通过新的工具和最佳实践来挑战隐私数据摄入的新工具和共享网络数据的潜力。该项目的数据将使计算机科学的新方法用于协议设计和网络安全，应用AI和机器学习，并将在汽车远程信息学领域快速发展的领域提供早期结果。 Pimawat将提供新的工具，数据和实践，并鼓励其他研究人员在教室和行业中使用这些方法。Pimawat项目网站将https://ant.isi.isi.edu/pimawat及其工具和数据集通过https://ttps://ant.isi.edu/datasets/inderforderion and and and and.法定使命，并通过评估诚实地认为，使用基金会的智力优点和更广泛的影响审查标准。