IEICE TRANSACTIONS on Information
Hide Association Rules with Fewer Side Effects
Summary :
Association rule mining is a powerful data mining tool, and it can be used to discover unknown patterns from large volumes of data. However, people often have to face the risk of disclosing sensitive information when data is shared with different organizations. The association rule mining techniques may be improperly used to find sensitive patterns which the owner is unwilling to disclose. One of the great challenges in association rule mining is how to protect the confidentiality of sensitive patterns when data is released. Association rule hiding refers to sanitize a database so that certain sensitive association rules cannot be mined out in the released database. In this study, we proposed a new method which hides sensitive rules by removing some items in a database to reduce the support or confidence levels of sensitive rules below specified thresholds. Based on the information of positive border rules and negative border rules contained in transactions, the proposed method chooses suitable candidates for modification aimed at reducing the side effects and the data distortion degree. Comparative experiments on real datasets and synthetic datasets demonstrate that the proposed method can hide sensitive rules with much fewer side effects and database modifications.
- Publication
- IEICE TRANSACTIONS on Information Vol.E98-D No.10 pp.1788-1798
- Publication Date
- 2015/10/01
- Publicized
- 2015/07/14
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2014EDP7345
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence, Data Mining
Authors
Peng CHENG
Harbin Institute of Technology,Southwest University
Ivan LEE
University of South Australia
Jeng-Shyang PAN
Harbin Institute of Technology
Chun-Wei LIN
Harbin Institute of Technology
John F. RODDICK
Engineering and Mathematics
Keyword
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Peng CHENG, Ivan LEE, Jeng-Shyang PAN, Chun-Wei LIN, John F. RODDICK, "Hide Association Rules with Fewer Side Effects" in IEICE TRANSACTIONS on Information,
vol. E98-D, no. 10, pp. 1788-1798, October 2015, doi: 10.1587/transinf.2014EDP7345.
Abstract: Association rule mining is a powerful data mining tool, and it can be used to discover unknown patterns from large volumes of data. However, people often have to face the risk of disclosing sensitive information when data is shared with different organizations. The association rule mining techniques may be improperly used to find sensitive patterns which the owner is unwilling to disclose. One of the great challenges in association rule mining is how to protect the confidentiality of sensitive patterns when data is released. Association rule hiding refers to sanitize a database so that certain sensitive association rules cannot be mined out in the released database. In this study, we proposed a new method which hides sensitive rules by removing some items in a database to reduce the support or confidence levels of sensitive rules below specified thresholds. Based on the information of positive border rules and negative border rules contained in transactions, the proposed method chooses suitable candidates for modification aimed at reducing the side effects and the data distortion degree. Comparative experiments on real datasets and synthetic datasets demonstrate that the proposed method can hide sensitive rules with much fewer side effects and database modifications.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2014EDP7345/_p
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@ARTICLE{e98-d_10_1788,
author={Peng CHENG, Ivan LEE, Jeng-Shyang PAN, Chun-Wei LIN, John F. RODDICK, },
journal={IEICE TRANSACTIONS on Information},
title={Hide Association Rules with Fewer Side Effects},
year={2015},
volume={E98-D},
number={10},
pages={1788-1798},
abstract={Association rule mining is a powerful data mining tool, and it can be used to discover unknown patterns from large volumes of data. However, people often have to face the risk of disclosing sensitive information when data is shared with different organizations. The association rule mining techniques may be improperly used to find sensitive patterns which the owner is unwilling to disclose. One of the great challenges in association rule mining is how to protect the confidentiality of sensitive patterns when data is released. Association rule hiding refers to sanitize a database so that certain sensitive association rules cannot be mined out in the released database. In this study, we proposed a new method which hides sensitive rules by removing some items in a database to reduce the support or confidence levels of sensitive rules below specified thresholds. Based on the information of positive border rules and negative border rules contained in transactions, the proposed method chooses suitable candidates for modification aimed at reducing the side effects and the data distortion degree. Comparative experiments on real datasets and synthetic datasets demonstrate that the proposed method can hide sensitive rules with much fewer side effects and database modifications.},
keywords={},
doi={10.1587/transinf.2014EDP7345},
ISSN={1745-1361},
month={October},}
Copy
TY - JOUR
TI - Hide Association Rules with Fewer Side Effects
T2 - IEICE TRANSACTIONS on Information
SP - 1788
EP - 1798
AU - Peng CHENG
AU - Ivan LEE
AU - Jeng-Shyang PAN
AU - Chun-Wei LIN
AU - John F. RODDICK
PY - 2015
DO - 10.1587/transinf.2014EDP7345
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2015
AB - Association rule mining is a powerful data mining tool, and it can be used to discover unknown patterns from large volumes of data. However, people often have to face the risk of disclosing sensitive information when data is shared with different organizations. The association rule mining techniques may be improperly used to find sensitive patterns which the owner is unwilling to disclose. One of the great challenges in association rule mining is how to protect the confidentiality of sensitive patterns when data is released. Association rule hiding refers to sanitize a database so that certain sensitive association rules cannot be mined out in the released database. In this study, we proposed a new method which hides sensitive rules by removing some items in a database to reduce the support or confidence levels of sensitive rules below specified thresholds. Based on the information of positive border rules and negative border rules contained in transactions, the proposed method chooses suitable candidates for modification aimed at reducing the side effects and the data distortion degree. Comparative experiments on real datasets and synthetic datasets demonstrate that the proposed method can hide sensitive rules with much fewer side effects and database modifications.
ER -
English
