The spread of data mining technologies has raised concerns in the area of data privacy and its impact on an average person. Data mining research community has, to some extent, paid attention to those concerns by designing a number of techniques and algorithms known collectively as Privacy-preserving Data Mining (PPDM). We will propose a taxonomy to describe the variety of contemporary PPDM research. Using this taxonomy, we will review state of the art in PPDM, including some of our own work. In particular, we will survey data modification techniques, algorithm modification techniques, and techniques  for the distributed data scenario. We will stress the need for an operational definition of data privacy, and will review some of the current proposals in this area. We will also briefly discuss some of the new PPDM work on data privacy in concerning data from personal mobile devices, and data from collaborative social networks.