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

在人工智能时代，访问大规模数据在推动科学和工程研究方面发挥着举足轻重的作用。然而，这些移动性数据中的大多数通常是专有的，因此除非由数据所有者发布，否则科学和工程社区无法访问或获取这些数据的成本很高。最近，在数据科学造福社会的背景下，一些数据所有者愿意与公众分享他们的移动数据，以解锁他们的价值观，但仍存在重大的隐私担忧。该项目背后的关键科学驱动因素是科学和工程社区缺乏经过消毒的多样化移动性数据访问与生成性机器学习和实用差异隐私的进步之间的差距。为了弥补这一差距，项目组构建了一个名为MobilityNet的可信网络基础设施(CI)仿真工具，以(1)通过强调MobilityNet的可信性(即，效用、隐私和公平性)的隐私保护生成性机器学习，基于真实数据合成现实的移动性数据；以及(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，以进行多学科创新。MobilityNet产生的数据有可能通过对多个科学和工程学科的研究产生重大的科学和社会影响，如计算机科学、交通工程、城市和区域规划、地理学、流行病学和经济学。该项目设计了MobilityNet，这是一个受行为启发的创新型CI工具，用于可靠的移动性数据合成(即平衡效用、隐私和公平性)。合成数据将通过三个CI组件生成，这些CI组件建立在经过验证的模型(如生成性机器学习和差异隐私)的基础上，从技术和人类两个方面：(1)跨域真实数据管理，以解决数据偏见；(2)社会知情的真实数据解释，以解决数据隐含性；以及(3)隐私保护的合成数据生成，以解决数据敏感性。MobilityNet的关键创新首先植根于社会科学，从用户研究中获得洞察力，以更好地了解所设计的CI工具在跨域环境中对效用、隐私和公平性的影响；然后通过一套在CI设计和实施方面具有社会信息的技术优点(例如，数据管理和生成)来实现；进一步使用可测量的指标对其进行评估；最后通过合成数据共享产生影响，使科学和工程界受益。MobilityNet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。

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.