Secure outsourcing SIFT: Efficient and Privacy-preserving Image Feature Extraction in the Encrypted Domain

Multimedia data needs huge storage space, and application of multimedia data needs powerful capability of computing. Cloud computing can help owner of multimedia data to deal with it. But, multimedia data on cloud may reveal privacy of data owner, such as sex, hobbies, address, looks, and so on. Data owner can encrypt multimedia data for confidentiality before uploading it to cloud. However, encrypted multimedia data makes its utilization difficult. In this paper, we first discover pre-existing schemes have problems of huge storage space, security and low efficiency due to their inefficient and insecure algorithms. Then, we provide an effective and practical privacy-preserving scale-invariant feature transform (SIFT) scheme for encrypted image. It uses leveled homomorphic encryption based on our new encoding schemes, our new homomorphic comparison, division and derivative encryption. Our new secure SIFT scheme can realize higher computing efficiency, greatly reduce communication costs and interactive times between user and server, and perform correct feature key point detection, accurate feature point description and image matching. We evaluate security and efficiency of our new secure SIFT scheme, and compare our new secure SIFT scheme with other schemes in detail. The result shows that it is closest to the original SIFT algorithm.