Sensitive Attributes based Privacy Preserving in Data Mining using k-anonymity

Data mining is the process of extracting interesting patterns or knowledge from huge amount of data. In recent years, there has been a tremendous growth in the amount of personal data that can be collected and analyzed by the organizations. Organizations such as credit card companies, real estate companies and hospitals collect and hold large volumes of data for their research purposes. E. g. National Institute of health. When these organizations publish data containing a lot of sensitive information. The importance of sharing data for research and knowledge discovery has been well-recognized. However, sharing data that contains sensitive personal information, such as insurance data, medical record, etc across organization boundaries can raise serious privacy concerns. There is a need to preserve the privacy of the individuals in data set . K-anonymity is one of the easy and efficient techniques to achieve privacy in many data publishing applications. In k-anonymity, all tuples of releasing database are generalized to make it anonymize which lead to data utility reduction and more information loss of publishing table. Sensitive attribute based anonymity method is very useful in preserving the privacy of individuals in organization's publication of data. It reduces information loss to the researchers by providing sensitive levels. This method also avoids Homogeneity attack and Background attacks.