A Security-Oriented Privacy-Preserving Framework for Efficient Medical Record Search in Telemedicine

In modern telemedicine and smart healthcare systems, secure retrieval of medical records is essential, as such data are highly sensitive and valuable for clinical decision-making. Existing privacy-preserving query frameworks protect user privacy but often incur high computational costs and limited efficiency when applied to large-scale datasets. This paper presents a lightweight privacy-preserving medical record search framework that integrates symmetric homomorphic encryption with a group-oriented query strategy, achieving both computational efficiency and strong privacy guarantees. Users generate encrypted query vectors under private parameters, ensuring that sensitive information remains concealed. The cloud server partitions the dataset into structured groups with predetermined identifiers, which narrows the search scope and reduces server-side computation. Encrypted queries are directly evaluated against grouped ciphertext records without exposing plaintext. Once relevant records are identified, the server derives a session key linked to the encrypted query and re-encrypts the results, enabling only legitimate users to verify and decrypt the outputs. The proposed framework enhances the timeliness of information acquisition under high-throughput conditions such as 5G, supporting rapid and large-scale data exchange. Security analysis confirms query confidentiality and resistance to unauthorized inference. Experimental evaluations demonstrate a improvement in query response times compared to state-of-the-art alternatives, alongside a substantial reduction in server-side processing load. These performance gains, coupled with compatibility for resource-constrained environments, position our framework as a scalable and efficient solution for large-scale remote e-healthcare systems.