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Erschienen in:
2008 | OriginalPaper | Buchkapitel
A Survey of Inference Control Methods for Privacy-Preserving Data Mining
verfasst von : Josep Domingo-Ferrer
Erschienen in: Privacy-Preserving Data Mining
Verlag: Springer US
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Inference control in databases, also known as Statistical Disclosure Control (SDC), is about protecting data so they can be published without revealing confidential information that can be linked to specific individuals among those to which the data correspond. This is an important application in several areas, such as official statistics, health statistics, e-commerce (sharing of consumer data), etc. Since data protection ultimately means data modification, the challenge for SDC is to achieve protection with minimum loss of the accuracy sought by database users. In this chapter, we survey the current state of the art in SDC methods for protecting individual data (microdata). We discuss several information loss and disclosure risk measures and analyze several ways of combining them to assess the performance of the various methods. Last but not least, topics which need more research in the area are identified and possible directions hinted.