Privacy preserving data mining using anonymization and K-means clustering on labor dataset
Privacy Preserving Data Mining (PPDM) has recently become an important research area. There are some issues and problems related to PPDM have been identified. Information loss occur when the original of data are modified to keep the privacy of those data. Effects of PPDM also cause the level of data...
Saved in:
Main Author: | Ahmad Zahari, Samahah Solehah |
---|---|
Format: | Thesis |
Language: | English |
Published: |
2019
|
Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/96295/1/SamahahSolehahMSC2019.pdf.pdf http://eprints.utm.my/id/eprint/96295/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:143456 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
State-of-the-art in privacy preserved k-anonymity revisited
by: Alsahib S. Aldeen, Yousra Abdul, et al.
Published: (2016) -
Privacy preserving data mining based on geometrical data transformation method (GDTM) and k-means clustering algorithm
by: Sirat @ Md. Siraj, Maheyzah, et al.
Published: (2018) -
Cloud based privacy preserving data mining model using hybrid k-anonymity and partial homomorphic encryption
by: Mansour Osman, Huda Osman
Published: (2022) -
Data anonymization using pseudonym system to preserve data privacy
by: Razak, S. A., et al.
Published: (2020) -
Collaborative healthcare information systems in privacy preservation based on K-anonymization model / Asmaa Hatem Rashid
by: Rashid, Asmaa Hatem
Published: (2014)