  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 of the original geolocated data (mobility data) for a sharing
purpose. We quantify the utility-privacy trade-off of mobility datasets in
terms of trajectory reconstruction risk, user re-identification risk, and
mobility predictability. Our proposed architecture reports a Pareto Frontier
analysis that enables the user to assess this trade-off as a function of
Lagrangian loss weight parameters. The extensive comparison results on four
representative mobility datasets demonstrate the superiority of our proposed
architecture and the efficiency of the proposed privacy-preserving features
extractor. Our results show that by exploring Pareto optimal setting, we can
simultaneously increase both privacy (45%) and utility (32%).
