Work

Federated TimeGAN for Privacy Preserving Synthetic Trajectory Generation

Year: 2024

Type: conference-paper

Abstract: Mobility datasets are crucial for various applications. However, sharing this data raises privacy concerns due to the sensitive nature of geolocation information. Synthetic data generation has recently emerged as a promising solution to protect geo-privacy of trajectory data. Current approaches rely on having a large set of authentic trajectories collected from individual users to train generative networks. However, this assumption proves impractical in many real-world scenarios due to the sensitive personal information typically embedded within trajectories. Our approach leverages federated learning to generate privacy-preserving synthetic trajectories without the need for centralized data collection. Experimental results demonstrate that our distributed framework effectively produces synthetic trajectories with distributions comparable to baseline, offering a privacy-conscious alternative for geo-privacy protection in mobility datasets.

Source: IEEE International Conference on Mobile Data Management (MDM)

DOI: 10.1109/mdm61037.2024.00062

Authors: Saloua Bouabba, Karine Zeitouni, Bassem Haidar, Nazim Agoulmine, Zaineb Chelly Dagdia

Institutions: Université de Versailles Saint-Quentin-en-Yvelines, Université Paris-Saclay, Informatique, BioInformatique, Systèmes Complexes

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: green

Funder: Agence Nationale de la Recherche