D2D Big Data Privacy-Preserving Framework Based on (a, k)-Anonymity Model

Jie Wang,Hongtao Li,Yifeng Cui,Feng Guo,Wenyin Zhang

doi:10.1155/2019/2076542

Jie Wang, Hongtao Li + Show 3 more

Open Access

https://doi.org/10.1155/2019/2076542

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Abstract

As a novel and promising technology for 5G networks, device-to-device (D2D) communication has garnered a significant amount of research interest because of the advantages of rapid sharing and high accuracy on deliveries as well as its variety of applications and services. Big data technology offers unprecedented opportunities and poses a daunting challenge to D2D communication and sharing, where the data often contain private information concerning users or organizations and thus are at risk of being leaked. Privacy preservation is necessary for D2D services but has not been extensively studied. In this paper, we propose an (a, k)-anonymity privacy-preserving framework for D2D big data deployed on MapReduce. Firstly, we provide a framework for the D2D big data sharing and analyze the threat model. Then, we propose an (a, k)-anonymity privacy-preserving framework for D2D big data deployed on MapReduce. In our privacy-preserving framework, we adopt (a, k)-anonymity as privacy-preserving model for D2D big data and use the distributed MapReduce to classify and group data for massive datasets. The results of experiments and theoretical analysis show that our privacy-preserving algorithm deployed on MapReduce is effective for D2D big data privacy protection with less information loss and computing time.

Highlights

Device-to-device (D2D) communications have been proposed as a promising technology for fifth generation (5G) cellular networks
With the rapid growth of mobile users and devices, D2D technology should be able to adapt to the delivery of massive amount of data across a large number of users. erefore, this paper proposes an (a, k)-anonymous D2D big data privacy-preserving framework deployed on MapReduce to provide rapid sharing, high accuracy on deliveries, efficient and intelligent delivery, and accurate content promotion to a large number of users
D2D communication has been proposed as a promising technology for 5G cellular networks. e data often contain sensitive information which should be protected during data transmission

Summary

Introduction

Device-to-device (D2D) communications have been proposed as a promising technology for fifth generation (5G) cellular networks. It has been shown that D2D communications can improve the network performance in terms of communication capacity and delay, spectral efficiency, power dissipation, and cellular coverage [1]. Recent studies have shown by mining the social and mobile behaviors of users that they prefer to share content offline via D2D communication [2,3,4]. With the rapid growth of mobile users and devices, D2D technology should be able to adapt to the delivery of massive amount of data across a large number of users. Erefore, this paper proposes an (a, k)-anonymous D2D big data privacy-preserving framework deployed on MapReduce to provide rapid sharing, high accuracy on deliveries, efficient and intelligent delivery, and accurate content promotion to a large number of users With the rapid growth of mobile users and devices, D2D technology should be able to adapt to the delivery of massive amount of data across a large number of users. erefore, this paper proposes an (a, k)-anonymous D2D big data privacy-preserving framework deployed on MapReduce to provide rapid sharing, high accuracy on deliveries, efficient and intelligent delivery, and accurate content promotion to a large number of users

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