MAXGDDP:decision data release with differential privacy

Jibin FU; Xiaojian ZHANG; Liping DING

doi:10.11959/j.issn.1000-436x.2018049

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MAXGDDP:decision data release with differential privacy

Papers | 更新时间：2024-06-05

- MAXGDDP:decision data release with differential privacy
- Journal on Communications Vol. 39, Issue 3, Pages: 136-146(2018)
- 作者机构：
  
  1. 河南财经政法大学计算机与信息工程学院，河南郑州 450046
  2. 中国科学院软件研究所，北京 100190
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61502146);The National Natural Science Foundation of China(91646203);The National Natural Science Foundation of China(91746115);The Natural Science Foundation of Henan Province(162300410006);The Key Technologies R＆D Program of Henan Province(142102210384);The Key Technologies R＆D Program of Henan Province(172102310713);The Research Program of The Higher Education of Henan Educational Committee(16A520002);Foundation for The Excellent Youth Teacher of Henan Province;The Young Talents Fund of Henan University of Economics and Law
- DOI：10.11959/j.issn.1000-436x.2018049
  CLC： TP309
- Online First：2018-03，
  
  Published：25 March 2018
- 稿件说明：
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Jibin FU, Xiaojian ZHANG, Liping DING. MAXGDDP:decision data release with differential privacy[J]. Journal on Communications, 2018, 39(3): 136-146.
DOI：

Jibin FU, Xiaojian ZHANG, Liping DING. MAXGDDP:decision data release with differential privacy[J]. Journal on Communications, 2018, 39(3): 136-146. DOI： 10.11959/j.issn.1000-436x.2018049.

摘要

基于层次细化的差分隐私决策数据发布得到了研究者的广泛关注，层次节点的选择、分类树的构建以及每层隐私代价的分配直接制约着决策数据发布结果的好坏，也影响最终的数据分析结果。针对现有基于层次细化的决策数据发布方法难以兼顾上述问题的不足，提出一种高效的分层细化方法MAXGDDP，该方法对原始分类数据进行分层细化，在同一层次的概念细化中提出了最大值属性索引算法，在不同层次之间利用类几何分配机制来更加合理地分配隐私预算。基于真实数据集对比了 MAXGDDP 与 DiffGen 算法，实验结果表明该方法在保护数据隐私的同时，提高了发布数据的分类准确率。

Abstract

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.

关键词

Keywords

references

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