Secure and Efficient Cross-Modal Retrieval over Encrypted Multimodal Data

With the popularity of social media, mobile devices and the Internet, a large amount of multimodal data (e.g, text, image, audio, video, etc.) is increasingly being outsourced to cloud to save local computing and storage costs. To search through encrypted multimodal data in the cloud, privacy-preserving cross-modal retrieval (PPCMR) techniques have attracted extensive attention. However, most of the existing PPCMR schemes lack the ability to resist quantum attacks and have low search efficiency on large-scale datasets. To solve above problems, we first propose a basic PPCMR scheme FECMR using the enhanced Single-key Function-hiding Inner Product Functional Encryption for Binary strings (SFB-IPFE) and cross-modal hashing technology, which achieves the measurement of similarity over encrypted multimodal data while resisting quantum attacks. Then, we design an efficient index KM-tree utilizing the K-modes clustering algorithm. On this basis, we propose an improved scheme FECMR+, which achieves sub-linear search complexity. Finally, formal security analysis proves that our schemes are secure against quantum attacks, and extensive experiments prove that our schemes are efficient and feasible for practical application.