An Efficient and Privacy-Preserving Cross-Modal Retrieval Scheme for Encrypted Data in the Cloud

The rapid development and widespread adoption of cloud computing have made privacy-preserving data retrieval in the cloud a hot topic in privacy computing research. While numerous schemes have been proposed for privacy-preserving text-to-text and image-to-image retrieval, there is a lack of research on cross-modal retrieval with privacy protection in the cloud, leaving ample room for enhancing the security and efficiency of existing schemes. To address these challenges, this paper proposes a scheme for privacy-preserving cross-modal retrieval. The proposed scheme leverages a cross-modal pretrained model to extract features from both text and images, which are then mapped into hash buckets using locality-sensitive hashing (LSH), thereby enhancing retrieval efficiency. Additionally, a data structure is employed to generate secure indexes for these hash buckets via symmetric searchable encryption (SSE). To further improve retrieval accuracy, a secure k-nearest neighbor (kNN) algorithm is applied for second-round ranking of the retrieval results. Analysis and extensive experiments on widely-used real-world datasets have demonstrated the scheme's effectiveness in preserving privacy while maintaining efficiency and feasibility.