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

PIMAWAT（Privacy in Internet Measurements Applied to WAN And Telecommunications）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，隐私和机器学习方面的新研究。主要的见解是 * 今天通过广域网（WAN）发送的大多数数据都是加密的 *;因此，面临的挑战是证明哪些数据被加密，检测和清除任何剩余的泄漏，并最终匿名化元数据PIMAWAT的智力价值将是开发大规模匿名网络流量的新方法，然后使用这些新算法来评估潜在的数据泄露，并证明在尊重隐私的同时，可以清理真实世界的数据源以进行共享。 PIMAWAT计划将调查员先前的工作重点放在广域网数据流量上。在可能的情况下，它还将探索车辆远程信息处理作为一个最近开发的数据集，带来独特的隐私机会和挑战，以设备（而不是人）为重点，但与地理位置和应用程序的细节。PIMAWAT的更广泛的影响将是民主化的潜力，收集和共享网络数据，通过新的工具和最佳实践尊重隐私的数据擦洗。 该项目的数据将应用人工智能和机器学习，为计算机科学的协议设计和网络安全提供新方法，并将在快速发展的车辆远程信息处理领域提供早期结果。PIMAWAT将提供新的工具、数据和实践，并鼓励其他研究人员、教室和行业使用这些方法。PIMAWAT项目网站将是https://ant.isi.edu/pimawat，其工具和数据集将在项目期间和项目完成后通过https://ant.isi.edu/datasets/提供。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。

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.