GAN-Driven personalized spatial-temporal private datasSharing in Cyber-Physical Social Systems

Qu, Youyang, Yu, Shui, Zhou, Wanlei and Tian, Yonghong 2020, GAN-Driven personalized spatial-temporal private datasSharing in Cyber-Physical Social Systems, IEEE Transactions on Network Science and Engineering, vol. 7, no. 4, Oct-Dec 2020, pp. 2576-2586, doi: 10.1109/TNSE.2020.3001061.

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Title GAN-Driven personalized spatial-temporal private datasSharing in Cyber-Physical Social Systems
Author(s) Qu, YouyangORCID iD for Qu, Youyang
Yu, Shui
Zhou, Wanlei
Tian, Yonghong
Journal name IEEE Transactions on Network Science and Engineering
Volume number 7
Issue number 4
Season Oct-Dec 2020
Start page 2576
End page 2586
Total pages 11
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2020-06-09
ISSN 2327-4697
Keyword(s) Science & Technology
Physical Sciences
Engineering, Multidisciplinary
Mathematics, Interdisciplinary Applications
Differential privacy
Social networking (online)
Data models
generative adversarial nets
personalized privacy protection
spatial-temporal data sharing
Summary The cyber-physical social system (CPSS) enables human social interaction from cyberspace to the physical world by sharing an increasing volume of spatial-temporal data. The sensitive information in the shared data is appealing to the adversaries and various attacks. However, most existing research assumes that the privacy protection level is identical regardless of various requirements, which is not practical. Subsequently, this results in either over-protection or failing to resist the leading attacks like collusion attacks. Motivated by this, a personalized model is proposed by using generative adversarial nets (GAN) to achieve differential privacy and thereby enhancing spatial-temporal private data sharing. A Differential Privacy Identifier is added to the classic GAN with a Generator and a Discriminator. The deployment of the differentially Private GAN (P-GAN) enables the generation of the sanitized data that can perfectly approximate the spatial-temporal trajectory while providing high-level privacy protection. P-GAN optimizes the trade-off between personalized privacy protection and improved data utility while maintaining fast convergence. Simultaneously, the random noise generation guaranteed by GAN breaks the correlation of injected noises and make P-GAN attack-proof against the collusion attacks. Extensive evaluation results on real-world datasets show the superiority of P-GAN from the aspects of optimized trade-off and efficiency.
Language eng
DOI 10.1109/TNSE.2020.3001061
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2020, IEEE
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