document: Transforming data to satisfy privacy constraints Data on individuals and entities are being collected widely . These data can contain information that explicitly identifies the individual ( e.g. , social security number ) . Data can also contain other kinds of personal information ( e.g. , date of birth , zip code , gender ) that are potentially identifying when linked with other available data sets . Data are often shared for business or legal reasons . This paper addresses the important issue of preserving the anonymity of the individuals or entities during the data dissemination process . We explore preserving the anonymity by the use of generalizations and suppressions on the potentially identifying portions of the data . We extend earlier works in this area along various dimensions . First , satisfying privacy constraints is considered in conjunction with the usage for the data being disseminated . This allows us to optimize the process of preserving privacy for the specified usage . In particular , we investigate the privacy transformation in the context of data mining applications like building classification and regression models . Second , our work improves on previous approaches by allowing more flexible generalizations for the data . Lastly , this is combined with a more thorough exploration of the solution space using the genetic algorithm framework . These extensions allow us to transform the data so that they are more useful for their intended purpose while satisfying the privacy constraints .
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