As a serious concern in data publishing and analysis, privacy preserving data processing has received a lot of attention. Privacy preservation often leads to information loss. Consequently, we want to minimize utility loss as long as the privacy is preserved. In this chapter, we survey the utility-based privacy preservation methods systematically. We first briefly discuss the privacy models and utility measures, and then review four recently proposed methods for utilitybased privacy preservation.
We first introduce the utility-based anonymization method for maximizing the quality of the anonymized data in query answering and discernability. Then we introduce the top-down specialization (TDS) method and the progressive disclosure algorithm (PDA) for privacy preservation in classification problems. Last, we introduce the anonymized marginal method, which publishes the anonymized projection of a table to increase the utility and satisfy the privacy requirement.
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Hua, M., Pei, J. (2008). A Survey of Utility-based Privacy-Preserving Data Transformation Methods. In: Aggarwal, C.C., Yu, P.S. (eds) Privacy-Preserving Data Mining. Advances in Database Systems, vol 34. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-70992-5_9
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DOI: https://doi.org/10.1007/978-0-387-70992-5_9
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