Verifiable Privacy-Preserving Retrieval Service for Large-Scale Image in Cloud Computing

The vigorous development of the Internet of Things and cloud computing is driving resource-limited smart devices to outsource large-scale images to cloud servers for storage and retrieval. Privacy-preserving image retrieval addresses the threat of data privacy leakage without affecting the searchability of images. Existing privacy-preserving retrieval schemes use the approximate nearest neighbor search to improve the retrieval efficiency of large-scale images on the cloud server. However, these schemes suffer from reduced retrieval accuracy, difficulties in constructing encrypted index structures, and a lack of result verification support. To tackle these problems, we propose a verifiable privacy-preserving retrieval scheme for large-scale images (VPIRL) in cloud servers. We use learning with errors (LWE) theory to protect image features, achieving distance and angle preservation between encrypted features. This enables the cloud server to construct an encrypted satellite system graph for efficient and accurate retrieval of large-scale images. We also propose a privacy-preserving data verification method based on the Merkle Hash Tree and cuckoo hash to detect dishonest behaviors of the cloud server and verify the correctness and completeness of the approximate nearest neighbor retrieval results. Experimental results show that this scheme achieves retrieval and verification in milliseconds for millions of images, confirming its practicality for large-scale image retrieval.