Privacy preserving data mining so far has mainly focused on the data collector 
scenario where individuals supply their personal data to an untrusted collector in 
exchange for value. In this scenario, random perturbation has proved to be very 
successful. An equally compelling, but overlooked scenario, is that of a data 
custodian, which either owns the data or is explicitly entrusted with ensuring 
privacy of individual data. In this scenario, we show that it is possible to 
minimize disclosure while guaranteeing no outcome change. We conduct our 
investigation in the context of building a decision tree and propose 
transformations that preserve the exact decision tree. We show with a detailed set 
of experiments that they provide substantial protection to both input data privacy 
and mining output privacy.