A Blockchain-Based Efficient Verification Scheme for Context Semantic-Aware Ciphertext Retrieval

In the age of big data, ensuring data privacy while enabling efficient encrypted data retrieval has become a critical challenge. Traditional searchable encryption schemes face difficulties in handling complex semantic queries. Additionally, they typically rely on honest but curious cloud servers, which introduces the risk of repudiation. Furthermore, the combined operations of search and verification increase system load, thereby reducing performance. Traditional verification mechanisms, which rely on complex hash constructions, suffer from low verification efficiency. To address these challenges, this paper proposes a blockchain-based contextual semantic-aware ciphertext retrieval scheme with efficient verification. Building on existing single and multi-keyword search methods, the scheme uses vector models to semantically train the dataset, enabling it to retain semantic information and achieve context-aware encrypted retrieval, significantly improving search accuracy. Additionally, a blockchain-based updatable master-slave chain storage model is designed, where the master chain stores encrypted keyword indexes and the slave chain stores verification information generated by zero-knowledge proofs, thus balancing system load while improving search and verification efficiency. Finally, an improved non-interactive zero-knowledge proof mechanism is introduced, reducing the computational complexity of verification and ensuring efficient validation of search results. Experimental results demonstrate that the proposed scheme offers stronger security, balanced overhead, and higher search verification efficiency.