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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
개인 정보 보호 연관 규칙 마이닝 알고리즘은 데이터 개인 정보를 유지하면서 데이터 변수 간의 관계를 발견하도록 설계되었습니다. 이 기사에서는 가짜 거래를 사용하여 연관 규칙 마이닝을 위해 최근 도입된 방식 중 하나를 수정합니다(fs). 특히 우리의 분석에 따르면 fs 이 방식은 합리적인 수준의 개인 정보 보호를 보장하기 위해 철저한 저장 공간과 높은 계산 요구 사항을 가지고 있습니다. 우리는 평균 사례 프라이버시의 이점을 활용하고 구조의 약점에 대한 연구에 동기를 부여하는 현실적인 프라이버시 정의를 소개합니다. fs 가짜 거래 필터링을 통해. 이 문제를 극복하기 위해 우리는 fs 개인 정보 보호와 자원을 동시에 고려하는 하이브리드 방식을 두 가지 지침으로 제시합니다. 분석적이고 경험적인 결과는 우리가 제안한 방식의 효율성과 적용 가능성을 보여줍니다.
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부
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
부
@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},}
부
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 -