Privacy-preservation techniques in data mining

Chunhua Su, Jianying Zhou, Feng Bao, Guilin Wang, Kouichi Sakurai

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In today’s information age, data collection is ubiquitous, and every transaction is recorded somewhere. The resulting data sets can consist of terabytes or even petabytes of data, so efficiency and scalability is the primary consideration of most data-mining algorithms. Data mining is becoming increasingly common in both the private and public sectors. Industries, such as banking, insurance, medicine, and retailing, commonly use data mining to reduce costs, enhance research, and increase sales. In the public sector, data-mining applications initially were used as a means to detect fraud and waste, but have grown to also be used for purposes, such as measuring and improving program performance.

Original languageEnglish
Title of host publicationDigital Privacy
Subtitle of host publicationTheory, Technologies, and Practices
PublisherCRC Press
Pages187-226
Number of pages40
ISBN (Electronic)9781420052183
ISBN (Print)9781420052176
Publication statusPublished - Jan 1 2007

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Economics, Econometrics and Finance(all)
  • Business, Management and Accounting(all)

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  • Cite this

    Su, C., Zhou, J., Bao, F., Wang, G., & Sakurai, K. (2007). Privacy-preservation techniques in data mining. In Digital Privacy: Theory, Technologies, and Practices (pp. 187-226). CRC Press.