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

Abstract

With the explosive growth in the number of pictures taken by smartphones, organizing and searchingpictures has become important tasks. To efficiently fulfill these tasks, the key enabler is annotating imageswith proper keywords, with which keyword-based searching and organizing become available for images.Currently, smartphones usually synchronize photo albums with cloud storage platforms, and have theirimages annotated with the help of cloud computing. However, the “offloading-to-cloud” solution may causeprivacy breach, since photos from smart photos contain various sensitive information. For privacy protection,existing research made effort to support cloud-based image annotation on encrypted images by utilizingcryptographic primitives. Nevertheless, for each annotation, it requires the cloud to perform linear checkingon the large-scale encrypted dataset with high computational cost.This paper proposes a cloud-assisted privacy-preserving image annotation with randomized k-d forest, namelyCPAR. With CPAR, users are able to automatically assign keywords to their images by leveraging the powerof cloud with privacy protected. CPAR proposes a novel privacy-preserving randomized k-d forest structure,which significantly improves the annotation performance compared with existing research. Thorough analysisis carried out to demonstrate the security of CPAR. Experimental evaluation on the well-known IAPR TC-12dataset validates the efficiency and effectiveness of CPAR.

Keywords

• image annotation
• privacy-preserving
• cloud computing