Presentation at Dies Academicus(HafenCity University)
Associate Professor Hamada delivered a keynote speech about AI for Climate-Resilient Urban Systems at Dies Academicus 2025 at HafenCity University Hamburg, Germany.
Publications & Talks
International Conference · 2023
Balancing Privacy and Utility of Spatio-Temporal Data for Taxi-Demand Prediction
Abstract
The growing demand for ride-hailing services has led to an increasing need for accurate taxi demand prediction. However, the use of real passenger data to train predictive models raises serious privacy concerns. To address this challenge, we present a privacy-preserving taxi demand prediction system that employs a generative model to synthesize synthetic trajectory data, preserving privacy while retaining the statistical properties of the original data. The system also overcomes the challenge of location dependence of latitude-longitude values by encoding the representation into region-independent space, making it more general and applicable to different geographical areas. The system was evaluated on real-world data collected from a major taxi service provider in Japan over a period of six months. The results showed that the system can effectively defend against 98% of all attempted attacks on passenger data and against 60% of state-of-the-art attacks on the learning-based prediction models. Additionally, the proposed system ensures the prediction performance, with a barely noticeable decrease of 2.9% compared to using the original data.
Related Projects
A Platform for Digitalizing Knowledge of Regional Communities
Japan Science and Technology Agency (JST) CREST
「基礎理論とシステム基盤技術の融合によるSociety 5.0のための基盤ソフトウェアの創出」領域
