DATA PRIVACY on E-HEALTH CARE SYSTEM

The main goal of this research is to develop and implement data privacy appropriate technique that fit with E-health system. Privacy preserving data is to develop methods without increasing the risk of misuse of the data used to generate those methods. A number of effective methods for privacy preserving data mining have been proposed. Privacy preservation of sensitive information is a key factor of current era. This study concludes a comprehensives analysis against trendy privacy control. Anonmization offers more privacy options rather to other privacy preservation techniques (Randomization, Encryption, and Sanitization). However, Anonmization itself contains several techniques that require concluding best one. According to the presented analysis there is close competition among KAnonymity, L-Diversity, T-Closeness, P-Sensitive and M-invariance. All these Anonmization methods offer resistance against prominent attacks like homogeneity and background. I conducted the analytical comparative analysis to select optimal Anonymization methods. Finally, the resulting algorithm has been implemented for both K-anonymity and L-Diversity algorithms with figures, charts and diagrams.