There is ongoing compulsion on health organization to share data for analyze purpose. The healthcare data includes patient behavior & records, DNA, laboratory test data, activity log, sensible data, cost data, and demographic data. Privacy becomes supplementally crucial in some scenarios when the data is shared with 3rd party along with the personal information of patients, and confidential record of healthcare organizations. Nonetheless, several suitable guidelines, privacy-preserving laws, and compliance requirements are there to safeguard electroclinic healthcare data. Although, privacy and security breaches remain key issues for healthcare systems. Anonymization techniques, however is liberate from the privacy related regulations. Machine learning models can imply on anonymized data. Thus, it generates an anonymized secure ML model, which provides greater protection against membership and attribute inference attacks. The heuristic approach results comparatively higher in accuracy where it does not violate data privacy and can be handled to train and test the model. Our security model’s results suggest that the proposed model makes the healthcare data system secure and unauthorized access to protected patient healthcare information almost impossible.