The success of data mining relies on the availability of high quality data. To ensure quality data mining, effective information sharing between organizations become a vital requirement in today’s society. Since data mining often involves person-specific and sensitive information like medical records, the public has expressed a deep concern about their privacy. Privacy-preserving data publishing is a study of eliminating privacy threats while, at the same time, preserving useful information in the released data for data mining. It is different from the study of privacy-preserving data mining which performs some actual data mining task. This thesis identifies a collection of privacy threats in real life data publishing, and presents a unified solution to address these threats.
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