Privacy-Preserving Image Denoising from External Cloud Databases

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

38 Scopus Citations
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Original languageEnglish
Article number7833136
Pages (from-to)1285-1298
Journal / PublicationIEEE Transactions on Information Forensics and Security
Issue number6
Online published25 Jan 2017
Publication statusPublished - Jun 2017


Along with the rapid advancement of digital image processing technology, image denoising remains a fundamental task, which aims to recover the original image from its noisy observation. With the explosive growth of images on the Internet, one recent trend is to seek high quality similar patches at cloud image databases and harness rich redundancy therein for promising denoising performance. Despite the well-understood benefits, such a cloud-based denoising paradigm would undesirably raise security and privacy issues, especially for privacy-sensitive image data sets. In this paper, we initiate the first endeavor toward privacy-preserving image denoising from external cloud databases. Our design enables the cloud hosting encrypted databases to provide secure query-based image denoising services. Considering that image denoising intrinsically demands high quality similar image patches, our design builds upon recent advancements on secure similarity search, Yao's garbled circuits, and image denoising operations, where each is used at a different phase of the design for the best performance. We formally analyze the security strengths. Extensive experiments over real-world data sets demonstrate that our design achieves the denoising quality close to the optimal performance in plaintext.

Research Area(s)

  • cloud computing, external database, Image denoising, privacy, security