CEIVS: A Scalable and Secure Encrypted Image Retrieval Scheme with Vertical Subspace Clustering

With the growing prominence of outsourcing services and increasing user concerns about security, existing encryption image retrieval schemes suffer from several limitations, such as low retrieval efficiency, poor retrieval accuracy, and inadequate scalability. Therefore, we propose an encrypted image retrieval scheme by dividing the clustering subspace with vertical parallels to construct indexes. Concretely, the data owners upload preprocessed classified datasets to a fully trusted auxiliary server. Subsequently, the auxiliary server clusters the images with the proximity relationships among their vector representations and employs the vertical bisecting lines of the key points to partition the clustered subspaces to encrypted indexes, namely the clustering encryption index of vertical subspace (CEIVS). This process efficiently filters out irrelevant images in advance, thereby enhancing the efficiency of encrypted retrieval. Ultimately, the Enhanced Asymmetric Scalar Product-preserving Encryption (E-ASPE) technique is adopted to ensure the security of the feature vectors, and a binary verification mechanism is proposed to detect data tampering, thereby ensuring the integrity of the retrieval results. While achieving data security, it is crucial to ensure that the results returned by the searchable encrypted image retrieval system align with the user queries, thereby enhancing the integrity of search results. Through comparative experimental analysis, this method outperforms existing solutions in terms of retrieval time and accuracy.