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Smart city article details
| Title | Privacy-Aware Human Mobility Prediction Via Adversarial Networks |
|---|---|
| ID_Doc | 43144 |
| Authors | Zhan Y.; Haddadi H.; Kyllo A.; Mashhadi A. |
| Year | 2022 |
| Published | Proceedings - 2nd International Workshop on Cyber-Physical-Human System Design and Implementation, CPHS 2022 |
| DOI | http://dx.doi.org/10.1109/CPHS56133.2022.9804533 |
| Abstract | As various mobile devices and location-based ser-vices 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 (i.e., 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%). © 2022 IEEE. |
| Author Keywords | data privacy; LSTM neural networks; mobility datasets; mobility prediction |
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