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

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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.