A Ciphertext Database Construction Scheme Based on an Improved Encrypted Index Construction

In the era of the digital economy, data has become a fundamental resource, a critical factor of production, and a key driver of socio-economic development. As the volume of data generated and collected increases, issues concerning data security and privacy protection have gained widespread attention. To enhance data security during storage and retrieval, this study proposes an improved ciphertext index construction scheme based on the Verifiable Delay Function (VDF) and Learning with Errors (LWE). The scheme employs k-flat partitioning to organize multidimensional structured data and constructs group-level ciphertext indexes based on attribute coverage values. VDF-generated salts and attribute-specific LWE keys derived through a Key Derivation Function (KDF) are combined with randomized encryption to reduce the correlation leakage associated with deterministic indexes. During retrieval, the server performs homomorphic subtraction on ciphertext indexes, while the client determines equality by comparing the resulting noise with a decision threshold. Role-based access control (RBAC) is incorporated to enforce attribute-level key isolation and unauthorized-access rejection. Experimental results demonstrate that the proposed scheme achieves effective ciphertext equality queries without directly exposing plaintext index values, while maintaining acceptable computational overhead.