CPAR: Cloud-Assisted Privacy-preserving Image Annotation with Randomized KD-Forest
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With the explosive growth in the number of pictures taken by smart phones, organizing and searching pictures has become important tasks. To efficiently fulfill these tasks, the key enabler is annotating images with proper keywords, with which keywords-based searching and organizing become available for images. Currently, smart phones usually synchronize photo albums with cloud storage platforms, and have their images annotated with the help of cloud computing. However, the "offloading-to-cloud" solution may cause privacy breach, since photos from smart photos contain various sensitive information. For privacy protection, our preliminary research made effort to support cloud-based image annotation on encrypted images by utilizing cryptographic primitives. Nevertheless, for each annotation, it requires the cloud to perform linear checking on the large-scale encrypted dataset with high computational cost. This paper addresses the challenge and proposes a cloud-assisted privacy-preserving image annotation with randomized kd-forest, namely CPAR. A novel privacy-preserving randomized kd-forest structure is proposed in CPAR as a secure and efficient index for the dataset, with which CPAR significantly improves the annotation performance. Thorough analysis is carried out to demonstrate the security of CPAR. Experimental evaluation on the well-known IAPR TC-12 dataset validates the efficiency and effectiveness of CPAR.
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