Privacy Preserving Data Mining Research: Current Status and Key Issues

  • Xiaodan Wu
  • Chao-Hsien Chu
  • Yunfeng Wang
  • Fengli Liu
  • Dianmin Yue
Conference paper

DOI: 10.1007/978-3-540-72588-6_125

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4489)
Cite this paper as:
Wu X., Chu CH., Wang Y., Liu F., Yue D. (2007) Privacy Preserving Data Mining Research: Current Status and Key Issues. In: Shi Y., van Albada G.D., Dongarra J., Sloot P.M.A. (eds) Computational Science – ICCS 2007. ICCS 2007. Lecture Notes in Computer Science, vol 4489. Springer, Berlin, Heidelberg

Abstract

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.

Keywords

Privacy preserving data mining 

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Xiaodan Wu
    • 1
  • Chao-Hsien Chu
    • 2
  • Yunfeng Wang
    • 1
  • Fengli Liu
    • 1
  • Dianmin Yue
    • 1
  1. 1.School of Management, Hebei University of Technology, Tianjin 300130China
  2. 2.College of Information Sciences and Technology, The Pennsylvania State University, 301K IST Building, University Park, PA 16802USA

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