Multi level privacy preserving in medical data publishing

Privacy preserving data publishing (PPDF) is an emerging technology in the datamining field which performs datamining operations in a secured manner to preserve the confidential/sensitive information. Preserving privacy while publishing medical information has become an important challenge in this area due to its high confidentiality. While publishing medical data the PPDF scheme should maximize the data utility at the same time should have a minimum data disclosure risk. This paper concerned with privacy of medical data while publishing the patient information for research or analysis purposes. K-anonymity and l-diversity are the most popular techniques used for preserving privacy. These techniques does not consider the semantic relationship between the data values so they are prone to similarity attack .In this paper, we present a privacy-preserving data publishing framework for publishing large datasets with the goals of providing different levels of utility to the users based on their access privileges. The proposed system overcome the similarity attack by applying a privacy preservation approach which uses a key attribute masking technique and an anonymization process.The results showed that the semantic anonymization increases the privacy level with effective data utility.