MK-WISE: Secure and Efficient Multi-Keyword Wildcard ABSE With Keyword-Level Revocation for Device–Edge–Cloud EHRs Data Sharing

The rapid proliferation of Internet of Things (IoT) in healthcare has transformed the management of Electronic Health Records (EHRs), but also introduced critical challenges in secure retrieval, dynamic revocation, and verifiable integrity over encrypted data. Existing Searchable Encryption (SE) and Attribute-Based Searchable Encryption (ABSE) models remain limited: (i) most support only exact or prefix keyword matching and cannot handle flexible wildcard or substring queries common in medical search; (ii) revocation is coarse-grained, often requiring costly key redistribution or ciphertext re-encryption; and (iii) integrity verification either incurs heavy blockchain overhead or exposes access structures, undermining privacy. To address these gaps, we proposeMK-WISE, a secure and efficient multi-keyword wildcard ABSE framework for IoT–EHR systems. MK-WISE integrates an Index–Wildcard Tree (IWT) with Substring Bloom Filters (SBF) to enable expressive wildcard and substring queries, employs a puncturable PRF–based revocation workflow with edge-local enforcement, hierarchical key updates, and optional blockchain anchoring, and incorporates homomorphic MACs for lightweight correctness and completeness verification. Security analysis proves that MK-WISE achieves confidentiality, keyword privacy, unlinkability, and revocability under standard assumptions. Experimental results demonstrate that MK-WISE significantly outperforms state-of-the-art schemes in trapdoor generation, search scalability, and revocation cost, achieving millisecond-level revocation without user disruption. These results highlight MK-WISE as a practical and comprehensive solution for privacy-preserving EHR retrieval in IoT-enabled healthcare.