SecureDL: A privacy preserving deep learning model for image recognition over cloud.

The key benefits of cloud services such as low cost, access flexibility, and mobility have attracted worldwide users to utilize deep learning algorithms for computer vision. These cloud servers are maintained by third parties, where users are always concerned about sharing their confidential data with them. In this paper, we addressed these concerns for by developing SecureDL , a privacy-preserving image recognition model for encrypted data over cloud. The proposed block-based image encryption scheme is well designed to protect image's visual information. The scheme constitutes an order-preserving permutation ordered binary number system and pseudo-random matrices. The proposed method is proved to be secure in a probabilistic viewpoint, and using various cryptographic attacks. Experiments are conducted over several image recognition datasets, and the trade-off analytics between the achieved recognition accuracy and data encryption is well described. SecureDL overcomes the storage and computational overheads that occur with fully-homomorphic and multi-party computation based secure recognition schemes. • The goal of this study is to analyze the trade-off relation between image privacy and recognition. • Order-preserving permutation ordered binary number system and pseudo-random matrices are used to achieve image security. • The proposed method is assessed with encrypted data over a variety of datasets. • Security analysis of the proposed method is established using mathematical proofs and standard tests. [ABSTRACT FROM AUTHOR]