Gan-based Differential Private Image Privacy Protection Framework for the Internet of Multimedia Things
journal contributionposted on 13.04.2021, 02:18 by J Yu, H Xue, Bo Liu, Y Wang, S Zhu, M Ding
With the development of the Internet of Multimedia Things (IoMT), an increasing amount of image data is collected by various multimedia devices, such as smartphones, cameras, and drones. This massive number of images are widely used in each field of IoMT, which presents substantial challenges for privacy preservation. In this paper, we propose a new image privacy protection framework in an effort to protect the sensitive personal information contained in images collected by IoMT devices. We aim to use deep neural network techniques to identify the privacy-sensitive content in images, and then protect it with the synthetic content generated by generative adversarial networks (GANs) with differential privacy (DP). Our experiment results show that the proposed framework can effectively protect users’ privacy while maintaining image utility.
This research was funded by the Science and Technology on Complex Electronic Simulation Laboratory Foundation under grant Number DXZT-JC-ZZ2017-005, the National Natural Science Foundation of China under grant Number 61802080, the Education Bureau of Guangzhou Municipality Higher Education Research Project under grant Number 201831827 and the Guangzhou University Research Project under grant Number RQ2020085.
Article NumberARTN 58
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Science & TechnologyPhysical SciencesTechnologyChemistry, AnalyticalEngineering, Electrical & ElectronicInstruments & InstrumentationChemistryEngineeringInternet of Multimedia Things (IoMT)image privacyobject detectiondeep learninggenerative adversarial networkdifferential privacyAnalytical Chemistry