Privacy-preserving threshold-based image retrieval in cloud-assisted Internet of Things

• Main Authors: SONG, Lin, MIAO, Yinbin, WENG, Jian, CHOO, Kim-Kwang Raymond, LIU, Ximeng, DENG, Robert H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Cloud computing; Cryptography; Data confidentiality; Encrypted image retrieval; Encryption; Feature extraction; Image retrieval; Internet of Things; Internet of Things (IoT); Privacy-preserving; Servers; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/6933; https://doi.org/10.1109/JIOT.2022.3142933
• Institution: Singapore Management University

Encrypted image retrieval is a promising technique for achieving data confidentiality and searchability the in cloud-assisted Internet of Things (IoT) environment. However, most of the existing top-k ranked image retrieval solutions have low retrieval efficiency, and may leak the values and orders of similarity scores to the cloud server. Hence, if a malicious server learns user background information through some improper means, then the malicious server can potentially infer user preferences and guess the most similar image content according to similarity scores. To solve the above challenges, we propose a privacy-preserving threshold-based image retrieval scheme using the convolutional neural network (CNN) model and a secure k-Nearest Neighbor (kNN) algorithm, which improves the retrieval efficiency and prevents the cloud server from learning the values and orders of similarity scores. Formal security analysis shows that our proposed scheme can resist both Ciphertext-Only-Attack (COA) and Chosen-Plaintext-Attack (CPA), and extensive experiments demonstrate that our proposed scheme is efficient and feasible for real-world datasets.