Invariant Post-Random Response Perturbation for Correlated Attributes Under Local Differential Privacy Constraint

YANG Gao-ming; ZHU Hai-ming; FANG Xian-jin; SU Shu-zhi

doi:10.3969/j.issn.0372-2112.2019.05.015

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Invariant Post-Random Response Perturbation for Correlated Attributes Under Local Differential Privacy Constraint

更新时间：2025-07-16

- Invariant Post-Random Response Perturbation for Correlated Attributes Under Local Differential Privacy Constraint
- Acta Electronica Sinica Vol. 47, Issue 5, Pages: 1079-1085(2019)
- 作者机构：
  
  安徽理工大学计算机科学与工程学院,安徽,淮南,232001
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61572034, No.61806006);Natural Science Foundation for Colleges and Universities in Anhui Province (No.KJ2018A0083, No.KJ2014A061);Major Science and Technology Project of Anhui Province (No.18030901025)
- DOI：10.3969/j.issn.0372-2112.2019.05.015
  CLC： TP391
- Published Online：25 May 2019，
  
  Published：2019
- 稿件说明：
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YANG Gao-ming, ZHU Hai-ming, FANG Xian-jin, et al. Invariant Post-Random Response Perturbation for Correlated Attributes Under Local Differential Privacy Constraint[J]. Acta Electronica Sinica, 2019, 47(5): 1079-1085.
DOI：

YANG Gao-ming, ZHU Hai-ming, FANG Xian-jin, et al. Invariant Post-Random Response Perturbation for Correlated Attributes Under Local Differential Privacy Constraint[J]. Acta Electronica Sinica, 2019, 47(5): 1079-1085. DOI： 10.3969/j.issn.0372-2112.2019.05.015.

摘要

本文研究敏感属性与部分准标识符属性存在相关时，如何有效减小重构攻击导致的隐私泄漏风险.首先，用互信息理论寻找原始数据集中对敏感属性具有强依赖关系的准标识符属性，为精确扰动数据属性提供理论依据；其次，针对关联属性和非关联属性，应用不变后随机响应方法分别对某个数据属性或者属性之间的组合进行扰动，使之满足局部

-差分隐私要求，并理论分析后数据扰动对隐私泄露概率和数据效用的影响；最后，实验验证所提算法的有效性和处理增量数据的能力，理论分析了数据结果.由实验结果可知，算法可以更好地达到数据效用和隐私保护的平衡.

Abstract

We investigate in this paper how to effectively reduce the risk of privacy leakage caused by refactoring attacks when the sensitive attributes and some quasi-identifier attributes are correlated.Firstly

the mutual information theory is used to find the quasi-identifier attributes which have strong dependence on the sensitive attributes in the original dataset

which provides a theoretical basis for accurately perturbing the data attributes.Secondly

for the correlated attributes and the non-correlated attributes

the invariant random response method is applied to perturb a certain data attribute or a combination of data attributes to satisfy the local

-differential privacy requirement.Theoretical anal

ysis of the impact of data perturbations on privacy leakage probability and data utility is also conducted.Finally

the experiment verifies the effectiveness of the proposed algorithm and its ability to process incremental data.The experimental results demonstrate that the algorithm can achieve a better balance between data utility and privacy protection.

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