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Balancing Privacy and Utility of Spatio-Temporal Data for Taxi-Demand Prediction. (arXiv:2305.08107v1 [cs.LG])
cs.CR updates on arXiv.org arxiv.org
Taxi-demand prediction is an important application of machine learning that
enables taxi-providing facilities to optimize their operations and city
planners to improve transportation infrastructure and services. However, the
use of sensitive data in these systems raises concerns about privacy and
security. In this paper, we propose the use of federated learning for
taxi-demand prediction that allows multiple parties to train a machine learning
model on their own data while keeping the data private and secure. This can
enable organizations to …
application city data demand important infrastructure machine machine learning operations prediction privacy privacy and security security sensitive data services systems temporal transportation utility