DPDeno: A Post-Processing Framework for Releasing Differentially Private Spatio-Temporal Mobility Features

The spatio-temporal (ST) mobility patterns derived from trajectory data are crucial for applications such as location-based services and urban analytics. However, releasing these mobility features raises significant privacy concerns, as they may expose sensitive personal location information. Differ...

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Vydáno v:IEEE transactions on information forensics and security Ročník 20; s. 10834 - 10848
Hlavní autoři: Sun, Xinyue, Liu, Xiaoyu, Ye, Qingqing, Hu, Haibo, Yang, Renyu, He, Hui, Zhang, Weizhe
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 2025
Témata:
Accuracy
Autoencoders
data privacy
Differential privacy
Noise
Noise measurement
Privacy
Refining
Roads
Training
Trajectory
trajectory data
trajectory features releasing
ISSN:1556-6013, 1556-6021
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Shrnutí:The spatio-temporal (ST) mobility patterns derived from trajectory data are crucial for applications such as location-based services and urban analytics. However, releasing these mobility features raises significant privacy concerns, as they may expose sensitive personal location information. Differential privacy (DP) is widely used to safeguard individual privacy during data releases, but existing methods for releasing ST features often suffer from utility loss because their high dimensionality requires injecting substantial noise to meet privacy guarantees. Several recent approaches attempt to address this issue by reducing noise in differentially private spatio-temporal (DPST) features, but they either discard valuable information while compressing noisy data representations or rely solely on restrictive road network topology constraints, resulting in only modest utility improvements. In this paper, we present DPDeno, a post-processing framework designed to significantly enhance the utility of DPST features. First, DPDeno generates synthetic trajectory datasets using public information (e.g., road network data) and applies existing DP methods to create paired DPST (noisy) and ST (clean) features. It then trains a spatio-temporal graph autoencoder (STGAE), which models each feature as a graph, with road segments as nodes and transitions over time as edges. By minimizing node- and edge-level reconstruction losses between the noisy and clean pairs, STGAE learns to refine DPST inputs toward the structural consistency of their clean counterparts, thereby improving their practical utility. The trained model is then used to post-process real DPST features. Importantly, DPDeno preserves the original DP guarantee, as STGAE is trained solely on synthetic data generated from public sources without accessing any private information. Experimental results on two real-world trajectory datasets show that DPDeno significantly improves both the statistical accuracy and practical usability of released mobility features.
ISSN:1556-6013
1556-6021
DOI:10.1109/TIFS.2025.3611106