Associative classification rules hiding for privacy preservation
Sensitive patterns could be discovered from the given data when the data are shared between business partners. Such patterns should not be disclosed to the other parties. However, the shared data should be credible and trustworthy for their 'quality'. In this paper, we address a problem of...
Saved in:
Main Authors: | Natwichai J., Sun X., Li X. |
---|---|
Format: | Article |
Language: | English |
Published: |
2014
|
Online Access: | http://www.scopus.com/inward/record.url?eid=2-s2.0-79956073972&partnerID=40&md5=bb36d3810623aa32abcd2cac40d50630 http://cmuir.cmu.ac.th/handle/6653943832/1574 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Chiang Mai University |
Language: | English |
Similar Items
-
Associative classification rules hiding for privacy preservation
by: Juggapong Natwichai, et al.
Published: (2018) -
A heuristic data reduction approach for associative classification rule hiding
by: Natwichai J., et al.
Published: (2014) -
Privacy preservation for associative classification
by: Harnsamut,N., et al.
Published: (2015) -
Data reduction approach for sensitive associative classification rule hiding
by: Juggapong Natwichai, et al.
Published: (2018) -
Data quality in privacy preservation for associative classification
by: Harnsamut N., et al.
Published: (2014)