Efficient Privacy-Preserving Similarity Retrieval With Fine-Grained Access Control

Privacy-preserving similarity retrieval for ciphertext images has broad applications in Internet of Things (IoT) areas, including smart healthcare, face recognition, and social networking. However, most existing privacy-preserving schemes suffer from inefficient retrieval and limited security guarantees due to the use of unreasonable encryption methods. In addition, those supporting fine-grained access control often lack scalability or are computationally inefficient. To address these challenges, we propose an efficient similarity retrieval scheme for ciphertext images that ensures both privacy preservation and fine-grained access control. First, we construct an encrypted index tree using clustering to improve retrieval efficiency while preserving high recall. Second, we achieve security against chosen-plaintext attacks (CPAs) and result verification by employing symmetric homomorphic encryption and Merkle hash tree. Third, we realize access control for each image, enabling simultaneous access verification and similarity retrieval via a single inner product operation. Our theoretical and experimental analysis shows that the proposed scheme is CPA-secure and achieves up to $100\times $ faster query processing than existing CPA-secure schemes, with the ability to retrieve 2000 ciphertext images within 0.5 s for 128-dimensional feature vectors.