  In medical organizations large amount of personal data are collected and
analyzed by the data miner or researcher, for further perusal. However, the
data collected may contain sensitive information such as specific disease of a
patient and should be kept confidential. Hence, the analysis of such data must
ensure due checks that ensure protection against threats to the individual
privacy. In this context, greater emphasis has now been given to the privacy
preservation algorithms in data mining research. One of the approaches is
anonymization approach that is able to protect private information; however,
valuable information can be lost. Therefore, the main challenge is how to
minimize the information loss during an anonymization process. The proposed
method is grouping similar data together based on sensitive attribute and then
anonymizes them. Our experimental results show the proposed method offers
better outcomes with respect to information loss and execution time.
