A malignant entity may access outsourced healthcare data in Internet of Things (IoT) and cloud environments during data sharing, analysis, and communication among the involved entities to procure sensitive information that can be misused or leaked to other illegal entities. To tackle this pivotal and challenging issue, this article presents a novel efficient, privacy-preserving, secure communication model (SeCoM) for healthcare data protection in the cloud and IoT systems by minimizing the threat of data leakage, identifying, and terminating malicious entities against data leakage, and addressing security threats. The model provides enhanced security to the system by synchronously performing secure data storage, analysis, sharing, and communication. The experimental results and comparison of the proposed model with existing approaches demonstrate that it significantly improves privacy, security, and detection efficiencies as well as data utilization of up to 9.49%, 77.17%, 83%, and 43.25% along with a reduction of leakage and unsafe communication links by 97.62% and 98%, respectively.