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With the explosive growth in the number of pictures taken by smartphones,organizing and searching pictures has become important tasks. To efficientlyfulfill these tasks, the key enabler is annotating images with proper keywords,with which keyword-based searching and organizing become available for images.Currently, smartphones usually synchronize photo albums with cloud storageplatforms, and have their images annotated with the help of cloud computing.However, the "offloading-to-cloud" solution may cause privacy breach, sincephotos from smart photos contain various sensitive information. For privacyprotection, existing research made effort to support cloud-based imageannotation on encrypted images by utilizing cryptographic primitives.Nevertheless, for each annotation, it requires the cloud to perform linearchecking on the large-scale encrypted dataset with high computational cost.This paper proposes a cloud-assisted privacy-preserving image annotation withrandomized kd-forest, namely CPAR. With CPAR, users are able to automaticallyassign keywords to their images by leveraging the power of cloud with privacyprotected. CPAR proposes a novel privacy-preserving randomized kd-foreststructure, which significantly improves the annotation performance comparedwith existing research. Thorough analysis is carried out to demonstrate thesecurity of CPAR. Experimental evaluation on the well-known IAPR TC-12 datasetvalidates the efficiency and effectiveness of CPAR.

CPAR: Cloud-Assisted Privacy-preserving Image Annotation with Randomized KD-Forest