A Survey of Quantification of Privacy Preserving Data Mining Algorithms Elisa Bertino, Dan Lin, and Wei Jiang Abstract The aim of privacy preserving data mining (PPDM) algorithms is to ex- tract 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 al- gorithms. In this chapter, we review and summarize existing criteria and metrics in evaluating privacy preserving techniques. 1 Introduction Privacy is one of the most important properties that an information system must sat- isfy. For this reason, several efforts have been devoted to incorporating privacy pre- serving techniques with data mining algorithms in order to prevent the disclosure of sensitive information during the knowledge discovery. The existing privacy preserv- ing data mining techniques can be classified according to the following five different dimensions [32]: (i) data distribution (centralized or distributed); (ii) the modifica- tion applied to the data (encryption, perturbation, generalization, and so on) in or- Elisa Bertino Purdue University, 305 N. University St., West Lafayette, IN, USA, e-mail: bertino@cs.purdue.edu Dan Lin Purdue University, 305 N. University St., West Lafayette, IN, USA, e-mail: lindan@cs.purdue.edu Wei Jiang Purdue University, 305 N. University St., West Lafayette, IN, USA, e-mail: wjiang@cs.purdue.edu 1