D2D Big Data Privacy-Preserving Framework Based on (a, k)-Anonymity Model
Joint Authors
Li, Hongtao
Wang, Jie
Guo, Feng
Zhang, Wenyin
Cui, Yifeng
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-08-07
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wang, Jie& Li, Hongtao& Guo, Feng& Zhang, Wenyin& Cui, Yifeng. 2019. D2D Big Data Privacy-Preserving Framework Based on (a, k)-Anonymity Model. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1194628
Modern Language Association (MLA)
Wang, Jie…[et al.]. D2D Big Data Privacy-Preserving Framework Based on (a, k)-Anonymity Model. Mathematical Problems in Engineering No. 2019 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-1194628
American Medical Association (AMA)
Wang, Jie& Li, Hongtao& Guo, Feng& Zhang, Wenyin& Cui, Yifeng. D2D Big Data Privacy-Preserving Framework Based on (a, k)-Anonymity Model. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1194628
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1194628