JST Meeting for AIP Challenge

In the era of Society 5.0, where technology harmonizes with human life to foster smarter, more sustainable societies, spatio-temporal data is essential in optimizing transportation, urban planning, and emergency management. However, leveraging this data responsibly requires rigorous privacy safeguards. Associate Professor Rizk's solution directly addresses this challenge with a privacy-preserving taxi-demand framework that aligns with Society 5.0 principles and supports Sustainable Development Goals (SDGs), particularly SDG 11: Sustainable Cities and Communities.
The proposed approach not only ensures compliance with global privacy standards, such as GDPR, but also demonstrates resilience against privacy attacks without compromising prediction accuracy. Real-world evaluations show that this system can provide reliable taxi-demand forecasts while safeguarding individual privacy, contributing to the development of smarter, safer, and more resilient urban environments.
This work is part of our CREST project, underscoring our lab’s commitment to advancing responsible AI solutions that support sustainable, human-centered urban futures