  The rapid growth of digital images motivates individuals and organizations to
upload their images to the cloud server. To preserve privacy, image owners
would prefer to encrypt the images before uploading, but it would strongly
limit the efficient usage of images. Plenty of existing schemes on
privacy-preserving Content-Based Image Retrieval (PPCBIR) try to seek the
balance between security and retrieval ability. However, compared to the
advanced technologies in CBIR like Convolutional Neural Network (CNN), the
existing PPCBIR schemes are far deficient in both accuracy and efficiency. With
more cloud service providers, the collaborative secure image retrieval service
provided by multiple cloud servers becomes possible. In this paper, inspired by
additive secret sharing technology, we propose a series of additive secure
computing protocols on numbers and matrices with better efficiency, and then
show their application in PPCBIR. Specifically, we extract CNN features,
decrease the dimension of features and build the index securely with the help
of our protocols, which include the full process of image retrieval in the
plaintext domain. The experiments and security analysis demonstrate the
efficiency, accuracy, and security of our scheme.
