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Privacy Preserving Decision Tree Learning Using Unrealized Data Sets
February 2012 (vol. 24 no. 2)
pp. 353-364
ASCII Text
x 
 
Pui K. Fong, Jens H. Weber-Jahnke, "Privacy Preserving Decision Tree Learning Using Unrealized Data Sets," IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 2, pp. 353-364, February, 2012.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2010.226,
author = {Pui K. Fong and Jens H. Weber-Jahnke},
title = {Privacy Preserving Decision Tree Learning Using Unrealized Data Sets},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {24},
number = {2},
issn = {1041-4347},
year = {2012},
pages = {353-364},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.226},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Privacy Preserving Decision Tree Learning Using Unrealized Data Sets
IS - 2
SN - 1041-4347
SP353
EP364
EPD - 353-364
A1 - Pui K. Fong,
A1 - Jens H. Weber-Jahnke,
PY - 2012
KW - Classification
KW - data mining
KW - machine learning
KW - security and privacy protection.
VL - 24
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Pui K. Fong, University of Victoria, Victoria
Jens H. Weber-Jahnke, University of Victoria, Victoria
Privacy preservation is important for machine learning and data mining, but measures designed to protect private information often result in a trade-off: reduced utility of the training samples. This paper introduces a privacy preserving approach that can be applied to decision tree learning, without concomitant loss of accuracy. It describes an approach to the preservation of the privacy of collected data samples in cases where information from the sample database has been partially lost. This approach converts the original sample data sets into a group of unreal data sets, from which the original samples cannot be reconstructed without the entire group of unreal data sets. Meanwhile, an accurate decision tree can be built directly from those unreal data sets. This novel approach can be applied directly to the data storage as soon as the first sample is collected. The approach is compatible with other privacy preserving approaches, such as cryptography, for extra protection.

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Index Terms:
Classification, data mining, machine learning, security and privacy protection.
Citation:
Pui K. Fong, Jens H. Weber-Jahnke, "Privacy Preserving Decision Tree Learning Using Unrealized Data Sets," IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 2, pp. 353-364, Feb. 2012, doi:10.1109/TKDE.2010.226
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