Recent advances in the Internet, in data mining, and in security technologies have gave rise to a new stream of research, known as privacy preserving data mining (PPDM). PPDM technologies allow us to extract relevant knowledge from a large amount of data, while hide sensitive data or information from disclosure. Several research questions have often being asked: (1) what kind of option available for privacy preserving? (2) Which method is more popular? (3) how to measure the performance of these algorithms? And (4) how effective of these algorithms in preserving privacy? To help answer these questions, we conduct an extensive review of 29 recent references from years 2000 to 2006 for analysis. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Wu, X., Chu, C. H., Wang, Y., Liu, F., & Yue, D. (2007). Privacy preserving data mining research: Current status and key issues. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4489 LNCS, pp. 762–772). Springer Verlag. https://doi.org/10.1007/978-3-540-72588-6_125
Mendeley helps you to discover research relevant for your work.