Secure and Verifiable Multikey Image Search in Cloud-Assisted Edge Computing

With the development of outsourcing services, users with limited resources tend to store encrypted images on remote servers and search them anytime and anywhere. However, existing encrypted image search schemes are proposed for cloud computing scenarios, and have some defects, such as excessive bandwidth resource consumption or network delay, which are not suitable for Internet of Things (IoT) devices in edge computing environment. Therefore, we propose a secure and verifiable multikey image search (SVMIS) scheme in cloud-assisted edge computing. First, the pretrained convolutional neural network model is employed to extract image feature vectors to improve search accuracy. Then, a key distribution protocol is designed to convert the encrypted indexes of different owners, and a transformation key list is constructed to support the multikey setting in edge computing. Next, the learning with errors based secure k-nearest neighbor algorithm is used to encrypt feature vectors to improve security. Finally, the Merkle hash tree is utilized to check the correctness of search results returned by edge servers. Theoretical analysis and extensive experiments using a real-world dataset evaluate the security and effectiveness of SVMIS.