CQED: Secure and efficient composite query processing over encrypted healthcare data

Technological advancements in IoT-driven smart healthcare, characterized by interconnected devices and massive outsourced data, have driven explosive data growth and increased the demand for composite queries that combine equality-range and fuzzy keyword searches. Due to limited local resources, data are increasingly outsourced to untrusted clouds, which exacerbates privacy risks. However, existing privacy-preserving mechanisms treat these query types separately, lacking seamless integration and creating barriers to secure execution. To address these challenges, we propose CQED, a secure and efficient composite query scheme over encrypted healthcare data. At its core, CQED employs a computationally unified homomorphic inner-product framework: fuzzy keyword matching encodes keywords into uni-gram vectors and measures similarity via squared Euclidean distance, while equality-range queries are transformed into inner-product calculations using 0-Encoding and 1-Encoding. Based on this unified design, we develop Secure Fuzzy Keyword Search (SFKS) and Secure Range Query (SRQ) algorithms to support composite queries directly over encrypted databases. In addition to query functionality, we further enhance privacy protection. Specifically, to mitigate single-dimensional privacy leakage in tree-based indexes, we introduce a Secure Retrieval Protocol (SRP) under a dual-cloud model, enabling verification of k-d tree query conditions without exposing plaintext information. In this way, CQED integrates query functionality with privacy preservation in a computationally unified framework. We provide formal security proofs for SFKS, SRQ, and CQED, and conduct extensive experiments on real datasets. The results demonstrate that CQED achieves both practicality and efficiency, confirming its utility for secure data outsourcing in privacy-compliant healthcare environments.