Preservation Of Patterns and Input-Output Privacy

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.