Privacy-Preserving Data Mining

Volume 34 of the series Advances in Database Systems pp 183-205

A Survey of Quantification of Privacy Preserving Data Mining Algorithms

  • Elisa BertinoAffiliated withDepartment of Computer Science, Purdue University
  • , Dan LinAffiliated withDepartment of Computer Science, Purdue University
  • , Wei JiangAffiliated withDepartment of Computer Science, Purdue University

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The aim of privacy preserving data mining (PPDM) algorithms is to extract relevant knowledge from large amounts of data while protecting at the same time sensitive information. An important aspect in the design of such algorithms is the identification of suitable evaluation criteria and the development of related benchmarks. Recent research in the area has devoted much effort to determine a trade-off between the right to privacy and the need of knowledge discovery. It is often the case that no privacy preserving algorithm exists that outperforms all the others on all possible criteria. Therefore, it is crucial to provide a comprehensive view on a set of metrics related to existing privacy preserving algorithms so that we can gain insights on how to design more effective measurement and PPDM algorithms. In this chapter, we review and summarize existing criteria and metrics in evaluating privacy preserving techniques.


Privacy metric