Privacy Preserving Association Rule Mining Algorithm Based on Hybrid Partial Hiding Strategy

Jianming Zhu () and Zhanyu Li ()
Additional contact information
Jianming Zhu: Central University of Finance and Economics
Zhanyu Li: Central University of Finance and Economics

A chapter in LISS 2013, 2015, pp 1065-1070 from Springer

Abstract: Abstract Privacy-preserving data mining (PPDM) is one of the newest trends in privacy and security research. It is driven by one of the major policy issues of the information ear – the right to privacy. In order to improve the privacy preservation of association rule mining, a hybrid partial hiding algorithm (HPH) is proposed. The original data set can be interference and transformed by different random parameters. Then, the algorithm of generating frequent items based on HPH is presented. Finally, it can be proved that the privacy of HPH algorithm is better than the original algorithm.

Keywords: Data mining; Association rule; Privacy preservation (search for similar items in EconPapers)
Date: 2015
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-40660-7_160

Ordering information: This item can be ordered from
http://www.springer.com/9783642406607

DOI: 10.1007/978-3-642-40660-7_160

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().