Differentially Private GANs for Generating Synthetic Indoor Location Data
arxiv(2024)
摘要
The advent of location-based services has led to the widespread adoption of
indoor localization systems, which enable location tracking of individuals
within enclosed spaces such as buildings. While these systems provide numerous
benefits such as improved security and personalized services, they also raise
concerns regarding privacy violations. As such, there is a growing need for
privacy-preserving solutions that can protect users' sensitive location
information while still enabling the functionality of indoor localization
systems. In recent years, Differentially Private Generative Adversarial
Networks (DPGANs) have emerged as a powerful methodology that aims to protect
the privacy of individual data points while generating realistic synthetic data
similar to original data. DPGANs combine the power of generative adversarial
networks (GANs) with the privacy-preserving technique of differential privacy
(DP). In this paper, we introduce an indoor localization framework employing
DPGANs in order to generate privacy-preserving indoor location data. We
evaluate the performance of our framework on a real-world indoor localization
dataset and demonstrate its effectiveness in preserving privacy while
maintaining the accuracy of the localization system.
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