Anonymizing Data Privacy Personalization and the Web

Personal information should not be exposed to other in any case. Personal data of patients is very sensitive. In other hand sharing of health reports are also necessary for research purpose. So here real challenge is how we can share the information in such way that the researchers can get maximum benefits of data without knowing any patients personal information. Maximizing data usage and minimizing privacy risk are two conflicting goals. Organizations always apply a set of transformations on their data before releasing it. While determining the best set of transformations has been the focus of extensive work in the database community, most of this work suffered from one or both of the following major problems scalability and privacy guarantee. In this project we used Differential Privacy which provides a theoretical formulation for privacy that ensures that the system essentially behaves the same way regardless of whether any individual is included in the database.