Denoising in the Dark: Privacy-Preserving Deep Neural Network-Based Image Denoising

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.