Privacy Preserving Human Mobility Clustering with Self-Organizing Trees

Abstract

The rapid growth of mobility data from phones, sensors, and connected systems has made it easier than ever to track and analyze how people move in the real world. This data can drive smarter decisions in urban planning, public health, and commercial services. At the same time, it raises tough privacy trade-offs. Individuals can be identified from even sparse data, especially with recent Trajectory User Linking (TUL) methods. In this paper, we implement a user segmentation algorithm for human mobility data designed to cluster individuals based on geospatial pattern-of-life data while censoring all personally identifiable information. Our algorithm addresses known privacy issues motivated by TUL approaches by extending a recent self-organizing tree model to represent a population of user trajectories rather than individual trees per user. This provides a hierarchical structure of user patterns of life across different geographical locations without exposing sensitive location details. Our findings indicate this method provides accurate clustering representations while balancing user privacy.

Keywords: Trajectory User Linking, Mobility Clustering, Privacy

DOI: 10.54941/ahfe1007191