SEcure Similar Image Matching (SESIM): An Improved Privacy Preserving Image Retrieval Protocol over Encrypted Cloud Database

The emergence of cloud computing provides new dimension for the user to perform computations and store huge amount of data say images, video, audio etc,. However, the benefits of outsourcing the tasks bring privacy issues for the data that are outsourced. Consequently, to ensure privacy, multimedia data is encrypted and offloaded to the cloud database. Though images are encrypted, during retrieval, cloud server performs similarity computation on plaintext features. Fully homomorphic shows great results in computation over encrypted data yet due to its computation burden it is not applicable for practical usage. Thus, to guarantee the secrecy of outsourced image features, the proposed paper introduced an efficient SEcure Similar Image Matching (SESIM) protocol under encrypted domain. The computation overhead of the proposed SESIM protocol is compared with the existing secure distance metrics. The experiments and performance analysis show the effectiveness and security of the proposed scheme under encrypted cloud database.