New technologies in the cloud, social media, the Internet of things (IoT), and E-healthcare systems all need privacy protection. Health and medical records, in terms of strategy, include images and health records about patients, and all personal details must be kept private to protect patients' privacy. Standard encryption systems for textual and structural one-dimensional data could not be specifically extended to e-health data due to weaknesses of digital data structures. When medical data is exchanged in the general public for various study purposes, an effective data retention system with minimum information loss is needed. This research has suggested a medical healthcare IoT’s-driven infrastructure with limited access based on the inspiration. Three techniques are used in the infrastructure. The first technique preserves a patient's sensitive information by quantifying the least amount of information lost during the anonymization phase. Based on the clustering principle, the technique has also designed data access that provides the public, doctors, and nurses access to a patient's sensitivity information. K-anonymity privacy protection depends on local encryption, which is based on cell suppression, which is the second suggested technique. This approach employs a mapping approach to divide the data into various regions in such a way that data from the same population is grouped. Finally, data processing techniques (such as k-means) are often used to filter data obtained from wireless sensor networks to make medical recommendations to doctors and patients. Many approaches, on the other hand, face a risk of data loss during the data handling process. Extensive simulations are used to compare the proposed algorithm's efficiency to that of the state-of-the-art algorithm. Simulation findings show that the suggested algorithms are beneficial in terms of an effective cluster forming in a small amount of time, minimal data loss, and data propagation execution time.