Abstract
As image data outsourcing to mobile cloud grows, data privacy has become a major concern. Privacy-preserving image retrieval aims to search over encrypted data without requiring decryption. However, existing schemes face three critical challenges: struggle to balance accuracy, efficiency, and security; limited scalability for large-scale image retrieval in multi-user settings; and vulnerability to security threats arising from user permission changes or key compromises. In this paper, we propose ELSEIR, a novel framework for accurate, efficient, and privacy-preserving image retrieval. ELSEIR leverages a deep hashing model to extract image feature vectors and designs an irreversible random hash code generation module that combines secure permutation keys with two differential privacy methods for privacy protection. To enable accurate searches in multi-user settings, ELSEIR introduces a key conversion protocol that allows the cloud to unify ciphertexts encrypted under different user keys via corresponding switch keys. Furthermore, we extend the framework to ABE-ELSEIR, which supports immediate and efficient user revocation. We further provide formal security proofs demonstrating that the proposed frameworks are resilient against known-plaintext and key collusion attacks. Extensive experiments on real-world datasets show that our scheme achieves accuracy comparable to the unprotected baseline, while surpassing existing approaches in both retrieval accuracy and efficiency.