GANs Based Density Distribution Privacy-Preservation on Mobility Data

Dan Yin,Qing Yang

doi:10.1155/2018/9203076

Abstract

With the development of mobile devices and GPS, plenty of Location-based Services (LBSs) have emerged in these years. LBSs can be applied in a variety of contexts, such as health, entertainment, and personal life. The location based data that contains significant personal information is released for analysing and mining. The privacy information of users can be attacked from the published data. In this paper, we investigate the problem of privacy-preservation of density distribution on mobility data. Different from adding noises into the original data for privacy protection, we devise the Generative Adversarial Networks (GANs) to train the generator and discriminator for generating the privacy-preserved data. We conduct extensive experiments on two real world mobile datasets. It is demonstrated that our method outperforms the differential privacy approach in both data utility and attack error.

Highlights

With the increasing popularity of mobile devices and GPS, plenty of Location-based Services (LBSs) have emerged in these years
We propose the density distribution privacypreservation on mobility data based on Generative Adversarial Networks (GANs)
(ii) Motivated by the applications of GANs on image processing, we train the generator and discriminator in GANs by random data and the original data and publish the data generated by the generator instead of the original data

Summary

Introduction

With the increasing popularity of mobile devices and GPS, plenty of Location-based Services (LBSs) have emerged in these years. LBSs can be applied in a variety of contexts, such as health, entertainment, and personal life. People can report their locations anywhere and anytime. People release tweets with their current locations on social networks; users share their running routines with their friends on the Internet. The location based data which includes significant personal information is often published for analysing and mining. The mobility data implies valuable personal information, such as home addresses, occupation, social relations, and interests. The identities can be interred from the locations where people often visit over a period of time, even their home addresses or occupations

Methods

Results

Conclusion