Towards Privacy-Preserving Semantic Mobility Analysis

By analyzing data reflecting human mobility, one can derive patterns and knowledge that are tightly linked to the underlying geography and therefore cannot be applied to another territory or even compared with patterns obtained for another territory. Another problem of mobility analysis is compromising personal privacy, since person identities can be determined based on the regularly visited geographical locations. We here propose an idea for novel approach based on transformation of the spatial component of movement data from the geographic space to an abstract semantic space, inspired by the concept of cartographic chorems. We demonstrate that many visual analytics procedures developed for geographic movement data can be adapted for privacy-preserving mobility analysis based on semantic spaces.