Privacy-Aware Adversarial Network in Human Mobility Prediction
File(s)
Working Paper
Author(s)
Zhan, Yuting
Haddadi, Hamed
Mashhadi, Afra
Type
Working Paper
Abstract
As 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. User
re-identification and other sensitive inferences are major privacy threats when
geolocated data are shared with cloud-assisted applications. Significantly,
four spatio-temporal points are enough to uniquely identify 95\% of the
individuals, which exacerbates personal information leakages. To tackle
malicious purposes such as user re-identification, we propose an 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. These representations aim to
maximally reduce the chance of user re-identification and full data
reconstruction with a minimal utility budget (i.e., loss). We train the
mechanism by quantifying privacy-utility trade-off of mobility datasets in
terms of trajectory reconstruction risk, user re-identification risk, and
mobility predictability. We report an exploratory analysis that enables the
user to assess this trade-off with a specific loss function and its weight
parameters. The extensive comparison results on four representative mobility
datasets demonstrate the superiority of our proposed architecture in mobility
privacy protection and the efficiency of the proposed privacy-preserving
features extractor. We show that the privacy of mobility traces attains decent
protection at the cost of marginal mobility utility. Our results also show that
by exploring the Pareto optimal setting, we can simultaneously increase both
privacy (45%) and utility (32%).
different smart city scenarios and applications, many unexpected privacy
leakages have arisen due to geolocated data collection and sharing. User
re-identification and other sensitive inferences are major privacy threats when
geolocated data are shared with cloud-assisted applications. Significantly,
four spatio-temporal points are enough to uniquely identify 95\% of the
individuals, which exacerbates personal information leakages. To tackle
malicious purposes such as user re-identification, we propose an 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. These representations aim to
maximally reduce the chance of user re-identification and full data
reconstruction with a minimal utility budget (i.e., loss). We train the
mechanism by quantifying privacy-utility trade-off of mobility datasets in
terms of trajectory reconstruction risk, user re-identification risk, and
mobility predictability. We report an exploratory analysis that enables the
user to assess this trade-off with a specific loss function and its weight
parameters. The extensive comparison results on four representative mobility
datasets demonstrate the superiority of our proposed architecture in mobility
privacy protection and the efficiency of the proposed privacy-preserving
features extractor. We show that the privacy of mobility traces attains decent
protection at the cost of marginal mobility utility. Our results also show that
by exploring the Pareto optimal setting, we can simultaneously increase both
privacy (45%) and utility (32%).
Date Issued
2022-09-05
Citation
2022
Publisher
ArXiv
Copyright Statement
©2022 The Author(s)
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (E
Engineering & Physical Science Research Council (E
Identifier
http://arxiv.org/abs/2208.05009v1
Grant Number
EP/N028260/2
EP/R0222091/1
PO: 20232790 (Ref: 301671)
EP/W005271/1
Subjects
cs.LG
cs.LG
cs.CR
cs.CY
Notes
15 pages, PoPETs'23, July 10--14, 2023, Lausanne, Switzerland. arXiv admin note: substantial text overlap with arXiv:2201.07519
Publication Status
Published