Does One Noise Fit All? Analyzing Utility in Transportation Mode-Based Anonymization

With the growing adoption of location-based services, ensuring user privacy without compromising data utility has become a critical research focus. In this paper, we investigate the impact of different statistical noise distributions-Laplace, Gaussian, Exponential, Cauchy, and Uniform, on trajectory anonymization across different transportation modes using Differential Privacy. We propose a modality-wise anonymization approach and assess its effectiveness through two down-stream tasks utility metric: (i) counting the number of unique users within a predefined area, and (ii) predicting modes of transport. Experimental results on the two public datasets Geolife and Roma taxi reveal that our modality based anonymization retains high utility, achieving up to 5% improvement in micro-F1 scores compared to single-noise based anonymization and preserves user counts (deviates by 4 users) closely as of original data. These findings emphasize that downstream tasks play a pivotal role in designing privacy-preserving mechanisms while maintaining the practical usability of mobility data.