On Variable Constraints in Privacy Preserving Data Mining

In recent years, privacy preserving data mining has become an important problem because of the large amount of personal data which is tracked by many business applications. In many cases, users are unwilling to provide personal information unless the privacy of sensitive information is guaranteed. A recent framework performs privacy preserving data mining by using a condensation based approach. In this framework, the privacy of all records is treated homogeneously. It is therefore inefficient to design a system with a uniform privacy requirement over all records. We discuss a new framework for privacy preserving data mining, in which the privacy of all records is not the same, but can vary considerably. This is often the case in many real applications, in which different groups of individuals may have different privacy requirements. We discuss a condensation based approach for privacy preserving data mining in which an efficient method is discussed for constructing the condensation in a heterogeneous way. The heterogeneous condensation is capable of handling both static and dynamic data sets. We present empirical results illustrating the effectiveness of the method.