<p>This research proposes a self-adaptive control mechanism for a smart IoT-based clinical massive data storage system that protects privacy. The goals are to protect information about patient medical conditions, implement access control for routine and emergencies, and facilitate privacy model to conserve the available space in massive information storage systems. Using a cross-domain access control policy, healthcare professionals from various clinical fields can safely transmit the secured health information created by the medical Internet of Things (IoT) when moved to the archive. Since local emergency medical care specialists are not allowed to gain access to the individual’s previous medical records, conventional access control software hinders the provision of first aid. At the same time, the patient’s life is in danger, enabling qualified data consumers to decode confidential health content. This work suggest a safe method known as Randomized Content-based privacy preservation model (RCPP) to create a unique two-fold access control technique that can be adapted in routine and emergency scenarios to address this issue. Patients’ past health information can be retrieved through a password-based break-glass access method in a crisis. Medical professionals with the appropriate feature secret keys may be granted data access privileges in routine utilization. A safe reduction technique is intended to remove redundant health records with similar information that could have been encoded with various access protocols to reduce storage expenses in a massive data storage infrastructure. The suggested method performed better than well-known methods. Together with achieving encryption and decryption times of 59 and 62 milliseconds, respectively, with a communication overhead of 68%, the proposed technique also achieved 94% correctness, 93% sensitivity, and 93% specificity.</p>