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Denoising in the Dark: Privacy-Preserving Deep Neural Network-Based Image Denoising
Zheng, Yifeng1,2; Duan, Huayi1; Tang, Xiaoting3; Wang, Cong1,2; Zhou, Jiantao4
2021-05-01
Source PublicationIEEE Transactions on Dependable and Secure Computing
ISSN1545-5971
Volume18Issue:3Pages:1261-1275
Abstract

Large volumes of images are being exponentially generated today, which poses high demands on the services of storage, processing, and management. To handle the explosive image growth, a natural choice nowadays is cloud computing. However, coming with the cloud-based image services is acute data privacy concerns, which has to be well addressed. In this paper, we present a secure cloud-based image service framework, which allows privacy-preserving and effective image denoising on the cloud side to produce high-quality image content, a key for assuring the quality of various image-centric applications. We resort to state-of-the-art image denoising techniques based on deep neural networks (DNNs), and show how to uniquely bridge cryptographic techniques (like lightweight secret sharing and garbled circuits) and image denoising in depth to support privacy-preserving DNN based image denoising services on the cloud. By design, the image content and the DNN model are all kept private along the whole cloud-based service flow. Our extensive empirical evaluation shows that our security design is able to achieve denoising quality comparable to that in plaintext, with high cost efficiency on the local side and practically affordable cost on the cloud side.

KeywordCloud Computing Deep Neural Networks Image Denoising Privacy Preservation
DOI10.1109/TDSC.2019.2907081
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science
WOS SubjectComputer Science, Hardware & Architecture ; Computer Science, Information Systems ; Computer Science, Software Engineering
WOS IDWOS:000650513000018
Scopus ID2-s2.0-85102377963
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Cited Times [WOS]:7   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Science and Technology
Corresponding AuthorWang, Cong
Affiliation1.Department of Computer Science, City University of Hong Kong, Hong Kong, Hong Kong
2.City University of Hong Kong, Shenzhen Research Institute, Shenzhen, 518057, China
3.Department of Computer Science, Brown University, Providence, 02912, United States
4.Department of Computer and Information Science, Faculty of Science and Technology, State Key Laboratory of Internet of Things for Smart City, University of Macau, 999078, Macao
Recommended Citation
GB/T 7714
Zheng, Yifeng,Duan, Huayi,Tang, Xiaoting,et al. Denoising in the Dark: Privacy-Preserving Deep Neural Network-Based Image Denoising[J]. IEEE Transactions on Dependable and Secure Computing,2021,18(3):1261-1275.
APA Zheng, Yifeng,Duan, Huayi,Tang, Xiaoting,Wang, Cong,&Zhou, Jiantao.(2021).Denoising in the Dark: Privacy-Preserving Deep Neural Network-Based Image Denoising.IEEE Transactions on Dependable and Secure Computing,18(3),1261-1275.
MLA Zheng, Yifeng,et al."Denoising in the Dark: Privacy-Preserving Deep Neural Network-Based Image Denoising".IEEE Transactions on Dependable and Secure Computing 18.3(2021):1261-1275.
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