Privacy-Preserving Clustering-Based Image Retrieval in Cloud-Assisted Internet of Things

The advancement of cloud-assisted Internet of Things (IoT) has amplified the usability for secure and searchable image retrieval, addressing the growing demand for privacy protection in digital multimedia. Current encrypted image retrieval schemes focus either on search precision or search efficiency, making them less viable for deployment on IoT devices with limited resources. Therefore, in this article, we present a Privacy-Preserving Clustering-Based Image Retrieval (PPCBIR) scheme for IoT environment. First, we employ a Convolutional Neural Networks (CNN) for feature extraction and design an extended k-Nearest Neighbor (kNN) algorithm to protect the privacy of image features. Then, we build a novel clustering-based hierarchical index tree structure to improve retrieval efficiency without compromising data privacy. Subsequently, a matrix re-encryption technique is implemented to achieve the availability of multi-terminal key distribution in IoT. Furthermore, we propose an index merging method that is scalable to index trees constructed by different data owners. Finally, formal security analysis demonstrates that PPCBIR is resistant to various threat models. Extensive experiments using authentic datasets indicate that our proposed scheme is comparable to linear retrieval in search accuracy and outperforms existing state-of-the-art schemes in search efficiency, and demonstrate its practicability in IoT.