Based on Inner Product Function Encryption Secure Cross-Modal Retrieval

As big data rapidly advances, the importance of cross-modal retrieval systems that protect user privacy becomes increasingly significant. Current common secure cross-modal retrieval methods primarily achieve privacy protection by designing feasible encryption schemes and constructing secure public mapping subspaces. However, these schemes often require a large amount of plaintext data for training by third parties, which can easily lead to the leakage of data owners' privacy. To address these security issues, this paper proposes a secure cross-modal retrieval model based on inner product function encryption (SCMR-IPE), which enables similarity measurement between encrypted multimodal data while effectively safeguarding data privacy. Additionally, it introduces a self-organizing mapping network and performs secure unsupervised clustering of high-dimensional feature vectors, thereby significantly reducing computational overhead. Finally, experimental results demonstrate that the proposal not only ensures user privacy but also provides efficient and reliable retrieval services, thereby exhibiting high practical application value.