Secure and Efficient Cross-Modal Data Retrieval in Internet of Things

With the rapid development of the Internet of Things (IoT), a large amount of multimodal medical data is outsourced to the IoT cloud to reduce local computation and storage costs. To retrieve encrypted multimodal medical data in cloud computing, privacy-preserving cross-modal retrieval (PPCMR) has attracted significant attention. However, existing PPCMR solutions are challenging to meet real-time retrieval requirements for large-scale datasets due to linear index structures and complex encryption algorithms. To solve these issues, we propose a novel PPCMR scheme, named CSCMR, based on canonical correlation analysis (CCA) and symmetric key hidden vector encryption (SHVE). First, we use locality-sensitive hashing and CCA to cluster multimodal medical data according to semantic similarity. Then, we build an index structure for Bloom filter based on the clustering results, achieving constant retrieval complexity. Finally, we encrypt each value in the filter using SHVE algorithm, protecting data privacy while reducing encryption computation overheads. Formal security analysis proves that our scheme can resist selective chosen-plaintext attack. Extensive experiments demonstrate that our scheme is effective and feasible.