Segmented Hash-Based Privacy-Preserving Image Retrieval Scheme in Cloud-Assisted IoT

The proliferation and application of the Internet of Things (IoT) have significantly increased the production and processing of large volumes of images. Due to privacy concerns, such data are often encrypted before being outsourced to third parties for subsequent retrieval and processing. Existing encrypted image retrieval schemes primarily aim to improve search accuracy but are limited in terms of retrieval efficiency and storage expenses, rendering them challenging to be implemented on IoT devices. To address these issues, we propose a segmented hash-based privacy-preserving image retrieval (SHPIR) scheme that enables accurate and efficient retrieval of encrypted images in IoT networks. First, we propose a feature extraction model based on the convolutional neural network (CNN) to generate low-dimensional segmented hash codes that represent both image categories and features accurately. Next, we design the cryptographic hierarchical index structure based on the segmented hash codes to improve retrieval efficiency and accuracy. We also design a secure Hamming distance computation algorithm and employ the learning with errors (LWEs)-based secure k-nearest neighbor (kNN) algorithm to preserve the privacy of feature vectors and file-access patterns. Finally, we provide a security analysis verifying that the SHPIR scheme can protect image privacy as well as indexing and query privacy. Additionally, we constructed a real IoT environment to test the practical effectiveness and applicability of our scheme. Experimental evaluation indicates that our proposed scheme outperforms existing state-of-the-art schemes in retrieval accuracy, search efficiency, and storage costs, making it more suitable for real-world IoT applications.