Privacy-Aware Human Mobility Prediction via Adversarial Networks. (arXiv:2201.07519v1 [cs.LG])

As various mobile devices and location-based services are increasingly
developed in different smart city scenarios and applications, many unexpected
privacy leakages have arisen due to geolocated data collection and sharing.
While these geolocated data could provide a rich understanding of human
mobility patterns and address various societal research questions, privacy
concerns for users' sensitive information have limited their utilization. In
this paper, we design and implement a novel LSTM-based adversarial mechanism
with representation learning to attain a privacy-preserving feature
representation …

arxiv human networks prediction privacy