Assessing Shadows in Mobility: Beyond Spatiotemporal Patterns

Human mobility patterns serve as a signature of individual behavior, offering insights into daily activities but also exposing significant privacy risks. Research has shown that even limited spatiotemporal data can uniquely identify most individuals, revealing their vulnerability to re-identification. This study extends beyond traditional spatiotemporal analysis by proposing a method to quantify behavioral vulnerability based on a user’s distinguishability in a multi-dimensional behavioral space. By analyzing users' proximity to their nearest neighbors using interpretable metrics, this approach aims to better understand and mitigate the privacy risks inherent in human mobility data.

BIFOLD AUTHORS

Dr. Anne Josiane Kouam