An Efficient Method for Knowledge Hiding Through Database Extension

In our era, Knowledge is not ¿just¿ information anymore, it is an asset. Data mining is thus extensively used for knowledge discovery from large data bases. The problem with the data mining is that with the availability of non-sensitive information that is not to be disclosed. Thus privacy is becoming an increasingly important issue in many data mining applications. A number of methods have recently been proposed for privacy preserving data mining of multidimensional data records. A number of techniques such as randomization and k-anonymity have been suggested in recent years in order to perform privacy-preserving data mining. Furthermore, the problem has been discussed in multiple communities such as the database community, the statistical disclosure control community and the cryptography community. We propose a new solution by integrating the advantages of both these techniques with the view of minimizing information loss and privacy loss. By making use of cryptographic techniques to store sensitive data and providing access to the stored data based on an individual´s role, we ensure that the data is safe from privacy breaches. The trade-off between data utility and data safety of our proposed method will be assessed.