MAXGDDP：基于差分隐私的决策数据发布算法

傅继彬; 张啸剑; 丁丽萍

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

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MAXGDDP：基于差分隐私的决策数据发布算法

论文Ⅱ：学术论文 | 更新时间：2024-06-05

- MAXGDDP：基于差分隐私的决策数据发布算法
- MAXGDDP:decision data release with differential privacy
- 通信学报 2018年39卷第3期页码：136-146
- 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：
  
  [ "傅继彬（1975-），男，河南许昌人，博士，河南财经政法大学副教授，主要研究方向为知识工程、机器学习、隐私保护等。" ]
  [ "张啸剑（1980-），男，河南周口人，博士，河南财经政法大学副教授，主要研究方向为隐私保护、差分隐私、数据库等。" ]
  [ "丁丽萍（1965-），女，山东青州人，中国科学院软件研究所研究员、博士生导师，主要研究方向为数字取证、系统安全、可信计算等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61502146);国家自然科学基金资助项目(91646203);国家自然科学基金资助项目(91746115);河南省自然科学基金资助项目(162300410006);河南省科技攻关基金资助项目(142102210384);河南省科技攻关基金资助项目(172102310713);河南省教育厅高等学校重点科研基金资助项目(16A520002);河南省青年骨干教师基金资助项目;河南财经政法大学青年拔尖人才资助计划基金资助项目
- DOI：10.11959/j.issn.1000-436x.2018049
  中图分类号： TP309
- 网络出版日期：2018-03，
  
  纸质出版日期：2018-03-25
- 稿件说明：
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傅继彬, 张啸剑, 丁丽萍. MAXGDDP：基于差分隐私的决策数据发布算法[J]. 通信学报, 2018,39(3):136-146.

Jibin FU, Xiaojian ZHANG, Liping DING. MAXGDDP:decision data release with differential privacy[J]. Journal on communications, 2018, 39(3): 136-146.
傅继彬, 张啸剑, 丁丽萍. MAXGDDP：基于差分隐私的决策数据发布算法[J]. 通信学报, 2018,39(3):136-146. DOI： 10.11959/j.issn.1000-436x.2018049.

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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