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
다중 민감 버킷화(MSB) 방법의 첫 번째 단계에서는 l- 여러 중요한 속성에 대한 다양성 그룹화가 불완전하여 더 많은 정보가 손실됩니다. 이 문제를 해결하기 위해 우리는 l-다양한 민감한 속성과 다차원 버킷의 회피에 대한 다양성 회피 세트 및 완전한 제안 l- 여러 민감한 속성에 대한 CLDG(다양성 그룹화) 알고리즘. 그런 다음 CLDG 알고리즘을 적용하여 MSB 알고리즘의 첫 번째 단계를 개선합니다. 실험 결과는 개선된 MSB 알고리즘의 첫 번째 단계의 그룹화 비율이 MSB 알고리즘의 원래 첫 번째 단계의 그룹화 비율보다 훨씬 높아 게시된 마이크로데이터의 정보 손실이 감소한다는 것을 보여줍니다.
Yuelei XIAO
Xi'an University of Post & Telecommunications
Shuang HUANG
Xi'an University of Post & Telecommunications
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부
Yuelei XIAO, Shuang HUANG, "Complete l-Diversity Grouping Algorithm for Multiple Sensitive Attributes and Its Applications" in IEICE TRANSACTIONS on Fundamentals,
vol. E104-A, no. 7, pp. 984-990, July 2021, doi: 10.1587/transfun.2020EAL2084.
Abstract: For the first stage of the multi-sensitive bucketization (MSB) method, the l-diversity grouping for multiple sensitive attributes is incomplete, causing more information loss. To solve this problem, we give the definitions of the l-diversity avoidance set for multiple sensitive attributes and the avoiding of a multiple dimensional bucket, and propose a complete l-diversity grouping (CLDG) algorithm for multiple sensitive attributes. Then, we improve the first stages of the MSB algorithms by applying the CLDG algorithm to them. The experimental results show that the grouping ratio of the improved first stages of the MSB algorithms is significantly higher than that of the original first stages of the MSB algorithms, decreasing the information loss of the published microdata.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2020EAL2084/_p
부
@ARTICLE{e104-a_7_984,
author={Yuelei XIAO, Shuang HUANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Complete l-Diversity Grouping Algorithm for Multiple Sensitive Attributes and Its Applications},
year={2021},
volume={E104-A},
number={7},
pages={984-990},
abstract={For the first stage of the multi-sensitive bucketization (MSB) method, the l-diversity grouping for multiple sensitive attributes is incomplete, causing more information loss. To solve this problem, we give the definitions of the l-diversity avoidance set for multiple sensitive attributes and the avoiding of a multiple dimensional bucket, and propose a complete l-diversity grouping (CLDG) algorithm for multiple sensitive attributes. Then, we improve the first stages of the MSB algorithms by applying the CLDG algorithm to them. The experimental results show that the grouping ratio of the improved first stages of the MSB algorithms is significantly higher than that of the original first stages of the MSB algorithms, decreasing the information loss of the published microdata.},
keywords={},
doi={10.1587/transfun.2020EAL2084},
ISSN={1745-1337},
month={July},}
부
TY - JOUR
TI - Complete l-Diversity Grouping Algorithm for Multiple Sensitive Attributes and Its Applications
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 984
EP - 990
AU - Yuelei XIAO
AU - Shuang HUANG
PY - 2021
DO - 10.1587/transfun.2020EAL2084
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E104-A
IS - 7
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - July 2021
AB - For the first stage of the multi-sensitive bucketization (MSB) method, the l-diversity grouping for multiple sensitive attributes is incomplete, causing more information loss. To solve this problem, we give the definitions of the l-diversity avoidance set for multiple sensitive attributes and the avoiding of a multiple dimensional bucket, and propose a complete l-diversity grouping (CLDG) algorithm for multiple sensitive attributes. Then, we improve the first stages of the MSB algorithms by applying the CLDG algorithm to them. The experimental results show that the grouping ratio of the improved first stages of the MSB algorithms is significantly higher than that of the original first stages of the MSB algorithms, decreasing the information loss of the published microdata.
ER -