SCRM: Secure and Controllable Similarity Retrieval in Multiuser Settings

Cloud computing has become an essential paradigm for facilitating large-scale and privacy-preserving encrypted image retrieval in Internet of Things (IoT) environments. However, existing encrypted image retrieval schemes face challenges in balancing retrieval efficiency and data security, which hinders their practical adoption. On one hand, retrieval-efficient schemes based on secure k-Nearest Neighbor (kNN) are prone to known-plaintext attacks; on the other hand, highly secure schemes based on homomorphic encryption often suffer from excessive computational and storage overhead. Furthermore, supporting multiuser environments and enforcing fine-grained access control over query users (QUs) are critical challenges in IoT-based retrieval systems. To tackle these issues, we propose a secure and controllable similarity retrieval scheme in multiuser settings (SCRM), which achieves a practical tradeoff between efficiency and security while enabling multiuser management. First, we design an efficient and privacy-preserving similarity computation method that is resilient against known-plaintext attacks. Second, we introduce a key conversion protocol that enables similarity retrieval in multiuser settings without requiring key sharing. Third, we integrate attribute-based encryption to enforce fine-grained access control and trace QUs who may leak decryption keys. A correctness analysis confirms that SCRM ensures accurate similarity retrieval while supporting access control. Furthermore, a formal security analysis demonstrates that SCRM effectively protects data privacy against known-plaintext attacks. Finally, extensive experiments on real-world image dataset validate the efficiency and effectiveness of SCRM.