Association rules mining is a frequently used technique which finds interesting association and correlation relationships among large set of data items which occur frequently together. Nowadays, data collection is ubiquitous in social and business areas. Many companies and organi¬zations want to do the collaborative association rules mining to get the joint benefits. However, the sensitive information leakage is a problem we have to solve and privacy- preserving techniques are strongly needed. In this paper, we focus on the privacy issue of the association rules mining and propose a secure frequent-pattern tree (FP-tree) based scheme to pre- serve private information while doing the collaborative association rules mining. We display that our schema is secure and collusion-resistant for n parties, which means that even if n - 1 dishonest party collude with a dishonest data miner in an attempt to learn the associations’ rules between honest respondents and their responses, they will be unable to success.
| Published in | Software Engineering (Volume 1, Issue 1) |
| DOI | 10.11648/j.se.20130101.11 |
| Page(s) | 1-6 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2013. Published by Science Publishing Group |
Association Rules, Privacy-Preserving, Cryptographic Protocol
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APA Style
S. Suresh, S. Uvaraj, N. Kannaiya Raja. (2013). To Allot Secrecy-Safe Association Rules Mining Schema Using FP Tree. Software Engineering, 1(1), 1-6. https://doi.org/10.11648/j.se.20130101.11
ACS Style
S. Suresh; S. Uvaraj; N. Kannaiya Raja. To Allot Secrecy-Safe Association Rules Mining Schema Using FP Tree. Softw. Eng. 2013, 1(1), 1-6. doi: 10.11648/j.se.20130101.11
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Download@article{10.11648/j.se.20130101.11,
author = {S. Suresh and S. Uvaraj and N. Kannaiya Raja},
title = {To Allot Secrecy-Safe Association Rules Mining Schema Using FP Tree},
journal = {Software Engineering},
volume = {1},
number = {1},
pages = {1-6},
doi = {10.11648/j.se.20130101.11},
url = {https://doi.org/10.11648/j.se.20130101.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.se.20130101.11},
abstract = {Association rules mining is a frequently used technique which finds interesting association and correlation relationships among large set of data items which occur frequently together. Nowadays, data collection is ubiquitous in social and business areas. Many companies and organi¬zations want to do the collaborative association rules mining to get the joint benefits. However, the sensitive information leakage is a problem we have to solve and privacy- preserving techniques are strongly needed. In this paper, we focus on the privacy issue of the association rules mining and propose a secure frequent-pattern tree (FP-tree) based scheme to pre- serve private information while doing the collaborative association rules mining. We display that our schema is secure and collusion-resistant for n parties, which means that even if n - 1 dishonest party collude with a dishonest data miner in an attempt to learn the associations’ rules between honest respondents and their responses, they will be unable to success.},
year = {2013}
}
TY - JOUR T1 - To Allot Secrecy-Safe Association Rules Mining Schema Using FP Tree AU - S. Suresh AU - S. Uvaraj AU - N. Kannaiya Raja Y1 - 2013/06/20 PY - 2013 N1 - https://doi.org/10.11648/j.se.20130101.11 DO - 10.11648/j.se.20130101.11 T2 - Software Engineering JF - Software Engineering JO - Software Engineering SP - 1 EP - 6 PB - Science Publishing Group SN - 2376-8037 UR - https://doi.org/10.11648/j.se.20130101.11 AB - Association rules mining is a frequently used technique which finds interesting association and correlation relationships among large set of data items which occur frequently together. Nowadays, data collection is ubiquitous in social and business areas. Many companies and organi¬zations want to do the collaborative association rules mining to get the joint benefits. However, the sensitive information leakage is a problem we have to solve and privacy- preserving techniques are strongly needed. In this paper, we focus on the privacy issue of the association rules mining and propose a secure frequent-pattern tree (FP-tree) based scheme to pre- serve private information while doing the collaborative association rules mining. We display that our schema is secure and collusion-resistant for n parties, which means that even if n - 1 dishonest party collude with a dishonest data miner in an attempt to learn the associations’ rules between honest respondents and their responses, they will be unable to success. VL - 1 IS - 1 ER -
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