MAXGDDP:decision data release with differential privacy

Specialization-based private decision data release has attracted considerable research attention in recent years.The relation among hierarchical node,taxonomy tree,and budget allocation directly constrains the accuracy of data release and classification.Most existing methods based on hierarchical sp...

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Main Authors: Jibin FU, Xiaojian ZHANG, Liping DING
Format: Article
Language:zho
Published: Editorial Department of Journal on Communications 2018-03-01
Series:Tongxin xuebao
Subjects:
Online Access:http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2018049/
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author Jibin FU
Xiaojian ZHANG
Liping DING
author_facet Jibin FU
Xiaojian ZHANG
Liping DING
author_sort Jibin FU
collection DOAJ
description Specialization-based private decision data release has attracted considerable research attention in recent years.The relation among hierarchical node,taxonomy tree,and budget allocation directly constrains the accuracy of data release and classification.Most existing methods based on hierarchical specialization cannot efficiently address the above problems.An effective method was proposed,called MAXGDDP to publish decision data with specialization.MAXGDDP employed MAX index attribute selection algorithm to select the highlight concept for furthering specialization in each hierarchy.Besides,for making more rational use of privacy budget,MAXGDDP relied on geometric strategy to allocate the privacy budget in each hierarchy.Compared with existing methods such as DiffGen on the real datasets,MAXGDDP outperforms its competitors,achieves data privacy and the better result of classification simultaneously.
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institution Kabale University
issn 1000-436X
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publishDate 2018-03-01
publisher Editorial Department of Journal on Communications
record_format Article
series Tongxin xuebao
spelling doaj-art-fe19ec08917a4d35a0594156afd790c42025-01-14T07:14:27ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2018-03-013913614659717155MAXGDDP:decision data release with differential privacyJibin FUXiaojian ZHANGLiping DINGSpecialization-based private decision data release has attracted considerable research attention in recent years.The relation among hierarchical node,taxonomy tree,and budget allocation directly constrains the accuracy of data release and classification.Most existing methods based on hierarchical specialization cannot efficiently address the above problems.An effective method was proposed,called MAXGDDP to publish decision data with specialization.MAXGDDP employed MAX index attribute selection algorithm to select the highlight concept for furthering specialization in each hierarchy.Besides,for making more rational use of privacy budget,MAXGDDP relied on geometric strategy to allocate the privacy budget in each hierarchy.Compared with existing methods such as DiffGen on the real datasets,MAXGDDP outperforms its competitors,achieves data privacy and the better result of classification simultaneously.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2018049/decision datadata releasedifferential privacyhierarchical specialization
spellingShingle Jibin FU
Xiaojian ZHANG
Liping DING
MAXGDDP:decision data release with differential privacy
Tongxin xuebao
decision data
data release
differential privacy
hierarchical specialization
title MAXGDDP:decision data release with differential privacy
title_full MAXGDDP:decision data release with differential privacy
title_fullStr MAXGDDP:decision data release with differential privacy
title_full_unstemmed MAXGDDP:decision data release with differential privacy
title_short MAXGDDP:decision data release with differential privacy
title_sort maxgddp decision data release with differential privacy
topic decision data
data release
differential privacy
hierarchical specialization
url http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2018049/
work_keys_str_mv AT jibinfu maxgddpdecisiondatareleasewithdifferentialprivacy
AT xiaojianzhang maxgddpdecisiondatareleasewithdifferentialprivacy
AT lipingding maxgddpdecisiondatareleasewithdifferentialprivacy