Differentially Private GANs for Generating Synthetic Indoor Location Data

TL;DR

This paper presents a framework using Differentially Private GANs to generate synthetic indoor location data, balancing privacy protection with localization accuracy in real-world scenarios.

Contribution

It introduces a novel indoor localization framework employing DPGANs to generate privacy-preserving synthetic location data.

Findings

Effective privacy preservation demonstrated on real-world data

Maintains localization accuracy with privacy guarantees

Outperforms baseline privacy-preserving methods

Abstract

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.