Privacy Preserving Trajectory

Privacy-preserving trajectory research focuses on protecting the sensitive location information embedded within individual movement data while maintaining data utility for applications like intelligent transportation systems and location-based services. Current research emphasizes generative models, such as variational autoencoders and generative adversarial networks, often combined with techniques like k-anonymity and differential privacy, to synthesize realistic yet anonymized trajectories. This field is crucial for balancing the societal benefits of analyzing mobility data with the fundamental right to location privacy, impacting both data-driven research and the development of responsible location-based technologies.