The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Les algorithmes d'exploration de règles d'association préservant la confidentialité ont été conçus pour découvrir les relations entre les variables dans les données tout en préservant la confidentialité des données. Dans cet article, nous révisons l'un des schémas récemment introduits pour l'exploration de règles d'association utilisant de fausses transactions (fs). En particulier, notre analyse montre que le fs Le système a des exigences de stockage exhaustives et de calcul élevées pour garantir un niveau raisonnable de confidentialité. Nous introduisons une définition réaliste de la vie privée qui bénéficie de la vie privée moyenne et motive l'étude d'une faiblesse dans la structure de la vie privée. fs par le filtrage des fausses transactions. Afin de surmonter ce problème, nous améliorons le fs en présentant un schéma hybride qui considère à la fois la vie privée et les ressources comme deux lignes directrices concurrentes. Les résultats analytiques et empiriques montrent l'efficacité et l'applicabilité du schéma proposé.
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Abedelaziz MOHAISEN, Nam-Su JHO, Dowon HONG, DaeHun NYANG, "Privacy Preserving Association Rule Mining Revisited: Privacy Enhancement and Resources Efficiency" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 2, pp. 315-325, February 2010, doi: 10.1587/transinf.E93.D.315.
Abstract: Privacy preserving association rule mining algorithms have been designed for discovering the relations between variables in data while maintaining the data privacy. In this article we revise one of the recently introduced schemes for association rule mining using fake transactions (fs). In particular, our analysis shows that the fs scheme has exhaustive storage and high computation requirements for guaranteeing a reasonable level of privacy. We introduce a realistic definition of privacy that benefits from the average case privacy and motivates the study of a weakness in the structure of fs by fake transactions filtering. In order to overcome this problem, we improve the fs scheme by presenting a hybrid scheme that considers both privacy and resources as two concurrent guidelines. Analytical and empirical results show the efficiency and applicability of our proposed scheme.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.315/_p
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@ARTICLE{e93-d_2_315,
author={Abedelaziz MOHAISEN, Nam-Su JHO, Dowon HONG, DaeHun NYANG, },
journal={IEICE TRANSACTIONS on Information},
title={Privacy Preserving Association Rule Mining Revisited: Privacy Enhancement and Resources Efficiency},
year={2010},
volume={E93-D},
number={2},
pages={315-325},
abstract={Privacy preserving association rule mining algorithms have been designed for discovering the relations between variables in data while maintaining the data privacy. In this article we revise one of the recently introduced schemes for association rule mining using fake transactions (fs). In particular, our analysis shows that the fs scheme has exhaustive storage and high computation requirements for guaranteeing a reasonable level of privacy. We introduce a realistic definition of privacy that benefits from the average case privacy and motivates the study of a weakness in the structure of fs by fake transactions filtering. In order to overcome this problem, we improve the fs scheme by presenting a hybrid scheme that considers both privacy and resources as two concurrent guidelines. Analytical and empirical results show the efficiency and applicability of our proposed scheme.},
keywords={},
doi={10.1587/transinf.E93.D.315},
ISSN={1745-1361},
month={February},}
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TY - JOUR
TI - Privacy Preserving Association Rule Mining Revisited: Privacy Enhancement and Resources Efficiency
T2 - IEICE TRANSACTIONS on Information
SP - 315
EP - 325
AU - Abedelaziz MOHAISEN
AU - Nam-Su JHO
AU - Dowon HONG
AU - DaeHun NYANG
PY - 2010
DO - 10.1587/transinf.E93.D.315
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2010
AB - Privacy preserving association rule mining algorithms have been designed for discovering the relations between variables in data while maintaining the data privacy. In this article we revise one of the recently introduced schemes for association rule mining using fake transactions (fs). In particular, our analysis shows that the fs scheme has exhaustive storage and high computation requirements for guaranteeing a reasonable level of privacy. We introduce a realistic definition of privacy that benefits from the average case privacy and motivates the study of a weakness in the structure of fs by fake transactions filtering. In order to overcome this problem, we improve the fs scheme by presenting a hybrid scheme that considers both privacy and resources as two concurrent guidelines. Analytical and empirical results show the efficiency and applicability of our proposed scheme.
ER -