Collaborative Research: IMR: MM-1B: Privacy-Preserving Data Sharing for Mobile Internet Measurement and Traffic Analytics

移动互联网测量对于网络设计、资源分配和网络故障排除至关重要。然而，共享移动互联网测量数据可能会危及用户隐私。鉴于人工智能在移动互联网测量和流量分析中的广泛引入，迫切需要数据共享解决方案，在数据质量、效用和数量之间进行权衡，提供可解释性的解决方案。为了缩小这一差距，该项目的目标是开发新的方法来增加数据，具有可解释的数据质量和实用性，以隐私保护的方式访问和共享收集的数据，并以智能和自主的方式协作分析互联网数据。这个合作项目汇集了来自内布拉斯加州大学林肯分校、犹他州立大学和威斯康星大学麦迪逊分校的研究人员。它旨在为具有隐私保护、协作和分布式智能以及自主性的移动互联网测量奠定坚实的基础。将制定可解释质量的数据合成和扩充、保护隐私的数据共享以及互联网测量数据的协作和保护隐私分析的方法和方法。此外，还将开发一个移动互联网流量生成器来评估所提出的方法。该项目可以显著推进互联网流量分析、质量可解释和隐私保护的数据处理、移动互联网流量分析、分布式人工智能和机器学习算法、优化、建模、仿真和试验台实验等方面的前期研究。与该项目相关的研究工作将极大地促进对下一代移动互联网测量关键问题的理解，利用分布式和协作智能来提供隐私保护数据共享和互联网流量分析。该项目的成果可以有力地促进我们的社会过渡到隐私和智能时代的数据共享。该项目将融合研究和教育，将新兴的移动互联网测量和隐私保护数据处理与6G无线系统、数据增强、人工智能和机器学习模型等高级主题引入三个合作机构的当前课程。该项目的网站位于：cns.unl.edu/imr-ppds。收集的数据、模拟代码和发布列表将在项目网站上发布。技术报告和被接受的手稿的副本也将在项目网站上公布。该网站将在项目期间维护，并在项目完成后至少两年内保持可访问。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Mobile Internet measurement is critical to network design, resource allocation, and troubleshooting network issues. However, sharing of mobile Internet measurement data can potentially compromise user privacy. Given the wide introduction of artificial intelligence to mobile Internet measurement and traffic analytics, there is an urgent need for data sharing solutions that provide explainability in terms of the trade-offs among data quality, utility and quantity. To close the gap, the objective of this project is to develop new methods to augment data with explainable data quality and utility, to access and share collected data in a privacy-preserving manner, and to collaboratively analyze Internet data with intelligence and autonomy.This collaborative project brings together investigators from University of Nebraska-Lincoln, Utah State University, and University of Wisconsin-Madison. It aims to lay a solid foundation for mobile Internet measurement with privacy preservation, collaborative and distributed intelligence, and autonomy. Methodologies and methods will be developed for quality-explainable data synthesis and augmentation; privacy-preserving data sharing; and collaborative and privacy-preserving analysis of Internet measurement data. Moreover, a mobile Internet traffic generator will be developed for evaluating the proposed methods. This project can significantly advance the prior research in Internet traffic analytics, quality-explainable and privacy-preserving data processing, mobile Internet traffic analytics, distributed artificial intelligence and machine learning algorithms, optimizations, modeling, simulations, and testbed experiments. The research efforts associated with this project will greatly advance the understandings of the critical issues in the next-generation mobile Internet measurement with distributed and collaborative intelligence to provide privacy-preserving data sharing and Internet traffic analytics. The outcomes of the project can potently foster the transition of our society into data sharing with privacy and intelligent era. Research and education will be integrated in this project by introducing emerging mobile Internet measurement and privacy-preserving data processing with advanced topics such as 6G wireless systems, data augmentation, artificial intelligence and machine learning models into the current curricula in the three collaborative institutions.The project website is hosted at: cns.unl.edu/imr-ppds. The collected data, simulation codes, and publication list will be published on the project website. Copies of technical reports and accepted manuscripts will also be published on the project website. The website will be maintained during the project years, and remain accessible for least 2 years after the completion of the project.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.