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

合作研究：IMR：MM-1B：移动互联网测量和流量分析的隐私保护数据共享

• 批准号: 2344341
• 负责人: Feng Ye
• 金额: $ 17万
• 依托单位: University of Wisconsin-Madison
• 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 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2344341&HistoricalAwards=false
• 关键词: Collaborative; Research; IMR; MM; 1B; Collaborative; Research; IMR; MM; 1B

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.

移动互联网测量对于网络设计，资源分配和解决网络问题的故障排除至关重要。但是，移动互联网测量数据的共享可能会损害用户隐私。鉴于人工智能向移动互联网测量和流量分析的广泛介绍，迫切需要数据共享解决方案，这些解决方案可根据数据质量，实用程序和数量之间的权衡提供解释性。为了缩小差距，该项目的目的是开发新的方法，以可解释的数据质量和实用程序来增强数据，以隐私性的方式访问和共享收集的数据，并以智能和自主权协作分析互联网数据。这项合作项目的调查员召集了内布拉斯加州林肯大学，犹他州大学，犹他州大学和Wissonsins和University of University of wisconsin和University of Wissons和University of wisconsIn和University of wissinsin和University。它旨在通过隐私保护，协作和分布式情报以及自主权为移动互联网测量奠定坚实的基础。将开发方法和方法，以用于质量可解释的数据综合和增强。保护隐私数据共享；以及互联网测量数据的协作和隐私分析。此外，将开发移动互联网流量生成器来评估所提出的方法。该项目可以显着提高Internet流量分析，质量和隐私数据处理，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，模拟和测试台实验的先前研究。与该项目相关的研究工作将大大提高对下一代移动互联网测量的关键问题的理解，并通过分布式和协作情报，以提供隐私保护数据共享和Internet流量分析。该项目的结果可以有效地促进我们社会与隐私和智能时代的数据共享的过渡。研究和教育将通过将新兴的移动互联网测量和隐私保护数据处理与高级主题（例如6G无线系统，数据增强，人工智能和机器学习模型）介绍到三个协作机构的当前课程中。收集的数据，仿真代码和出版物列表将发布在项目网站上。技术报告和接受手稿的副本也将在项目网站上发布。该网站将在项目几年内维护，并在项目完成后至少可访问2年。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来获得支持的。