Privacy-preserving transfer learning-based secure quantum image retrieval in encrypted domain for cloud environment

Nowadays, the rapid growth of personal handheld electronic devices encourages the individual and organizations to upload their information to cloud for storage and processing purposes. To ensure privacy, the images are encrypted using cryptographic schemes that are outsourced to the cloud. Though images are encrypted, searching for similar images leads the cloud server to access the image data as it performs computation over plaintext. Thus, to ensure the privacy of the images during image retrieval, the proposed framework presents a transfer learning-based secure quantum image retrieval scheme over encrypted cloud. The confidentiality of the images is guaranteed by introducing quantum-based image encryption. Meanwhile, clustered image feature vectors are extracted through the transfer learning model and protected using secure multiparty computation. During retrieval, the proposed system introduces a similarity comparison model for performing computation on encrypted data. The experiments and performance analysis show the effectiveness and security of the proposed scheme.