IEICE TRANSACTIONS on Fundamentals
Complete l-Diversity Grouping Algorithm for Multiple Sensitive Attributes and Its Applications
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E104-A No.7 pp.984-990
- Publication Date
- 2021/07/01
- Publicized
- 2021/01/12
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2020EAL2084
- Type of Manuscript
- LETTER
- Category
- Cryptography and Information Security
Authors
Yuelei XIAO
Xi'an University of Post & Telecommunications
Shuang HUANG
Xi'an University of Post & Telecommunications
Keyword
Latest Issue
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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
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@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},}
Copy
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 -
English
