Collaborative Research: Frameworks: MobilityNet: A Trustworthy CI Emulation Tool for Cross-Domain Mobility Data Generation and Sharing towards Multidisciplinary Innovations

协作研究：框架：MobilityNet：用于跨域移动数据生成和共享以实现多学科创新的值得信赖的 CI 仿真工具

• 批准号: 2411152
• 负责人: Guang Wang
• 金额: $ 37万
• 依托单位: Florida State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2411152&HistoricalAwards=false
• 关键词: Collaborative; Research; Frameworks; MobilityNet; Trustworthy

In the era of Artificial Intelligence, accessing large-scale data plays a pivotal role in advancing Science and Engineering research. However, most of these mobility data are often proprietary and thus cannot be accessed or are costly to acquire by the Science and Engineering community unless released by data owners. Recently, under the context of Data Science for Social Good, some data owners are willing to share their mobility data with the public to unlock their values, but there are significant privacy concerns that remain in the way. The key science driver behind this project is the gap between the lack of sanitized diverse mobility data access for the Science and Engineering community and the advance of generative Machine Learning and practical differential privacy. To bridge this gap, the project team builds a trustworthy cyberinfrastructure (CI) emulation tool called MobilityNet to (1) synthesize realistic mobility data based on real data, via privacy-preserving generative machine learning emphasizing MobilityNet's trustworthiness (i.e., utility, privacy, and fairness); and (2) share these data with the Science and Engineering community for multidisciplinary innovations by working with 19 partners. The data generated by MobilityNet have the potential for significant scientific and societal impacts via research in multiple Science and Engineering disciplines such as Computer Science, Transportation Engineering, Urban and Regional Planning, Geography, Epidemiology, and Economics. This project designs MobilityNet, an innovative behavior-inspired generative CI tool for trustworthy mobility data synthesis (i.e., balancing utility, privacy, and fairness). The synthetic data will be generated via three CI components built upon validated models (such as generative machine learning and differential privacy) from both technical and human aspects: (1) cross-domain real data curation to address data bias, (2) socially-informed real data interpretation to address data implicitness, and (3) privacy-preserving synthetic data generation to address data sensitivity. The key innovation of MobilityNet is first grounded in social science, where insights are drawn from user studies to better understand the impact of the designed CI tool on utility, privacy, and fairness in a cross-domain setting; it is then materialized with a set of socially-informed technological merits on CI design and implementation (e.g., data curation and generation); it is further evaluated with measurable metrics; it finally creates impacts through synthetic data sharing to benefit the Science and Engineering community. The research vision in MobilityNet will contribute to the success of the national CI Ecosystem.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.

在人工智能时代，访问大规模数据在推进科学和工程研究中起着关键作用。 但是，除非数据所有者发布，否则这些移动性数据中的大多数通常都是专有的，因此无法访问科学和工程界的昂贵。 最近，在社会善良的数据科学的背景下，一些数据所有者愿意与公众共享其流动性数据以释放其价值，但是仍然存在很大的隐私问题。 该项目背后的关键科学驱动力是科学和工程社区缺乏消毒多样化的移动性数据访问与生成机器学习的进步与实践差异隐私之间的差距。为了弥合这一差距，该项目团队建立了一个可信赖的网络基础架构（CI）仿真工具，称为MobilityNet，以（1）通过实际数据合成现实的移动性数据，这是通过隐私性的生成机器学习强调移动性网络的可信赖性（即，公用事业，隐私和公平）； （2）通过与19个合作伙伴合作，与科学和工程社区共享这些数据，以用于多学科创新。 MobilityNet产生的数据有可能通过在多个科学和工程学科（例如计算机科学，运输工程，城市和区域规划，地理，流行病学和经济学）等多个科学和工程学科的研究中产生重大的科学和社会影响。该项目设计MobilityNet，这是一种创新的行为启发性生成CI工具，用于可信赖的移动性数据综合（即平衡效用，隐私和公平）。合成数据将通过技术和人类方面的三个CI组件（例如生成机器学习和差异隐私）构建的三个CI组件生成：（1）跨域的真实数据策划来解决数据偏见，（2）社会化的真实数据解释以解决数据的隐私数据生成数据的隐私数据敏感性。 MobilityNet的关键创新首先以社会科学为基础，在该社会科学中，从用户研究中获取见解，以更好地了解设计的CI工具对跨域环境中效用，隐私和公平性的影响；然后，它具有有关CI设计和实施（例如数据策划和生成）的一组社会知名的技术优点；通过可测量的指标进一步评估。它最终通过合成数据共享产生影响，从而使科学和工程社区受益。移动网络中的研究愿景将有助于国家CI生态系统的成功。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。