Geolocated Data Generation and Protection Using Generative Adversarial Networks

Alatrista-Salas, Hugo; Montalvo-Garcia, Peter; Núñez-del-Prado, Miguel; Salas, Julián

doi:10.1007/978-3-031-13448-7_7

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…The method was tested on a taxi dataset, demonstrating its capabilities. With a focus on ϵ-differential privacy, Alatrista-Salas et al [28] conducted a study showing the applicability of Differential Privacy Generative Adversarial Networks on mobility data on a GPS level, but also that the risk of re-identification still persists. If such methods are used for sharing, the original data must not be merged and shared with the synthetic data.…”

Section: Knowledge Driven Data Driven Potential City Widementioning

confidence: 99%

Privacy Preserving Human Mobility Generation using Grid based Data and Graph Autoencoders

Netzler,

Lienkamp

2024

Preprint

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The proposed method deals with the problem of data privacy and sharing when processing personal mobility tracking data. Previous methods concentrate on producing highly detailed data on short-term and restricted areas, e.g. for autonomous driving scenarios. Another possibility consists of city-wide scales and beyond, that are used to predict general traffic flows. The presented approach takes the tracked mobility behavior of individuals to create coherent new mobility data that reflects the long-term mobility behavior of the person, guaranteeing location persistency and sound embedding within the point-of-interest structure of the observed area. After an analysis and clustering step with the original data, the area is distributed into a geospatial grid structure (H3 is used here), and the neighborhood relationships between the grids are interpreted as a graph. A feed-forward-autoencoder and a graph encoding-decoding network generate a latent space representation of the area. The original clustered data is associated with their respective H3 grids. With a greedy algorithm approach and concerning privacy strategies, new combinations of grids are top-level patterns for individual mobility behavior. Concrete locations are found and connected within the grids based on the original data. The described method is then applied to a study with 1000 participants from the city of Munich in Germany, and the results are described, showing the application of the approach in generating synthetic data, enabling further research on individual mobility behavior and patterns. The result is a sharable dataset on the same abstraction level as the input data, which makes it interesting, particularly for machine learning applications.

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Section: Knowledge Driven Data Driven Potential City Widementioning

confidence: 99%

Privacy Preserving Human Mobility Generation using Grid based Data and Graph Autoencoders

Netzler,

Lienkamp

2024

Preprint

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Generative Models for Synthetic Urban Mobility Data: A Systematic Literature Review

2023

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Although highly valuable for a variety of applications, urban mobility data is rarely made openly available as it contains sensitive personal information. Synthetic data aims to solve this issue by generating artificial data that resembles an original dataset in structural and statistical characteristics, but omits sensitive information. For mobility data, a large number of corresponding models have been proposed in the last decade. This systematic review provides a structured comparative overview of the current state of this heterogeneous, active field of research. A special focus is put on the applicability of the reviewed models in practice.

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Privacy Preserving Human Mobility Generation Using Grid-Based Data and Graph Autoencoders

Netzler,

Lienkamp

2024

IJGI

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This paper proposes a one-to-one trajectory synthetization method with stable long-term individual mobility behavior based on a generalizable area embedding. Previous methods concentrate on producing highly detailed data on short-term and restricted areas for, e.g., autonomous driving scenarios. Another possibility consists of city-wide and beyond scales that can be used to predict general traffic flows. The now-presented approach takes the tracked mobility behavior of individuals and creates coherent synthetic mobility data. These generated data reflect the person’s long-term mobility behavior, guaranteeing location persistency and sound embedding within the point-of-interest structure of the observed area. After an analysis and clustering step of the original data, the area is distributed into a geospatial grid structure (H3 is used here). The neighborhood relationships between the grids are interpreted as a graph. A feed-forward autoencoder and a graph encoding–decoding network generate a latent space representation of the area. The original clustered data are associated with their respective H3 grids. With a greedy algorithm approach and concerning privacy strategies, new combinations of grids are generated as top-level patterns for individual mobility behavior. Based on the original data, concrete locations within the new grids are found and connected to ways. The goal is to generate a dataset that shows equivalence in aggregated characteristics and distances in comparison with the original data. The described method is applied to a sample of 120 from a study with 1000 participants whose mobility data were generated in the city of Munich in Germany. The results show the applicability of the approach in generating synthetic data, enabling further research on individual mobility behavior and patterns. The result comprises a sharable dataset on the same abstraction level as the input data, which can be beneficial for different applications, particularly for machine learning.

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Geolocated Data Generation and Protection Using Generative Adversarial Networks

Cited by 3 publications

References 18 publications

Privacy Preserving Human Mobility Generation using Grid based Data and Graph Autoencoders

Privacy Preserving Human Mobility Generation using Grid based Data and Graph Autoencoders

Generative Models for Synthetic Urban Mobility Data: A Systematic Literature Review

Privacy Preserving Human Mobility Generation Using Grid-Based Data and Graph Autoencoders

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