Generalization Technique for Privacy Preserving of Medical information

Abstract—the development in health information technology have increased the need share data and knowledge especially medical information, researchers, public health organizations, others interested in medical data. Some medical records are often added and deleted in the practical applications. The leakage of privacy information caused by re-publishing datasets with multiple sensitive attributes becomes more likely than any other publication styles This paper attempts to fill the above gaps, presents a framework for de-identifying health information .we used the generalization technique and K-Anonymization model to protecting privacy of patient data. After the protecting privacy applicable prepare the data for analysis and extract the knowledge that supports decision-making, we offer a range of initial assessments show the effectiveness-of our-approach.