Authenticated Certificateless Verifiable Searchable Public Key Encryption Scheme with Big Data Analytics for IoT-Based Healthcare

The emerging concept of Internet of Things (IoT)-based healthcare Industry 5.0 emphasizes the integration of human intelligence with cutting-edge technologies, like Artificial Intelligence (AI), blockchain, IoT, big data analytics, and machine learning (ML) models to revolutionize healthcare delivery. However, alongside the immense potential and opportunities of healthcare 5.0, the rapid expansion of sensitive medical data within the cloud-based healthcare systems also brings significant challenges related to data security, privacy, and resource requirements. Since most healthcare infrastructures depend on the semi-trusted centralized cloud storage, it then becomes crucial to store medical records in encrypted form in order to ensure confidentiality and privacy of the data. At the same time, these systems must provide seamless and efficient search capabilities for authorized medical personnel in healthcare system. To address these concerns and enable cost-effective, secure access and data management, we propose a novel authenticated certificateless verifiable searchable public key encryption scheme (ACLV-SPKE) that emerges as a robust and promising solution for securing healthcare data. The proposed framework not only resists diverse adversarial attacks but also ensures efficiency in terms of communication and computation costs, while offering improved functionality over recent state-of-the-art solutions presented in literature. The proposed scheme effectively resists both inside and outside keyword guessing attacks, achieving strong security guarantees like ciphertext and trapdoor indistinguishability, which are proved in the random oracle models. In addition, we applied big data analytics on a real healthcare dataset to evaluate the performance metrics, and the outcomes are presented in this article.