Assessment method on protection effectiveness of differential privacy algorithms based on fuzzy influence diagram

TIAN Yuechi; LI Fenghua; ZHOU Zejun; SUN Zhe; GUO Shoukun; NIU Ben

doi:10.11959/j.issn.1000-436x.2024122

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Assessment method on protection effectiveness of differential privacy algorithms based on fuzzy influence diagram

Papers | 更新时间：2024-09-10

- Assessment method on protection effectiveness of differential privacy algorithms based on fuzzy influence diagram
- Journal on Communications Vol. 45, Issue 8, Pages: 1-19(2024)
- 作者机构：
  
  1.中国科学院信息工程研究所,北京 100085
  2.中国科学院大学网络空间安全学院,北京 100049
  3.网络空间安全防御重点实验室,北京 100085
  4.广州大学网络空间安全学院,广东广州 510006
- 作者简介：
- 基金信息：
  
  Major Programs of the National Social Science Foundation of China(22&ZD147)
- DOI：10.11959/j.issn.1000-436x.2024122
  CLC： TN92
- Received：23 February 2024，
  
  Revised：2024-06-11，
  
  Published：25 August 2024
- 稿件说明：
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田月池,李凤华,周泽峻等.基于模糊影响图的差分隐私算法保护效果评估方法[J].通信学报,2024,45(08):1-19.

TIAN Yuechi,LI Fenghua,ZHOU Zejun,et al.Assessment method on protection effectiveness of differential privacy algorithms based on fuzzy influence diagram[J].Journal on Communications,2024,45(08):1-19.
田月池,李凤华,周泽峻等.基于模糊影响图的差分隐私算法保护效果评估方法[J].通信学报,2024,45(08):1-19. DOI： 10.11959/j.issn.1000-436x.2024122.

TIAN Yuechi,LI Fenghua,ZHOU Zejun,et al.Assessment method on protection effectiveness of differential privacy algorithms based on fuzzy influence diagram[J].Journal on Communications,2024,45(08):1-19. DOI： 10.11959/j.issn.1000-436x.2024122.

摘要

针对隐私保护算法实际保护效果评估难的问题，提出了一种基于模糊影响图的差分隐私算法保护效果评估方法，实现对差分隐私算法的多维度评估，得出保护效果综合分数和等级。从算法安全性、算法可行性、隐私偏差性、数据可用性和用户体验5个方面出发，建立指标体系。使用模糊理论处理不确定性，通过影响图传递影响关系并计算该模糊影响图，得出保护效果分数和等级，据此反馈调整算法参数，实现迭代评估。提出正向化环节，解决截然相反的算法在某些情况下评估结果一样的问题。电-碳模型中的对比实验表明，所提方法能够对差分隐私算法的保护效果做出有效评价，消融实验进一步表明，正向化环节对算法的区分度起了关键作用。

Abstract

In response to the challenge of comprehensively assessing privacy-preserving algorithms

an assessment method on protection effectiveness of differential privacy algorithms based on fuzzy influence diagram was proposed

achieving a multi-perspective assessment of differential privacy algorithms with a comprehensive score and level as assessment results. Starting from five aspects—algorithm security

feasibility

privacy bias

data utility

and user experience

an indicator system was established. Fuzzy theory was employed to handle uncertainties

while the diagram was used to propagate interactions between factors. The assessment score and level were obtained by calculating the fuzzy influence diagram

and then used as feedback for parameter adjustment to achieve iterative assessment. Formalization link was proposed to solve the problem of completely opposite algorithms with idential evaluation results. Comparative experiments on electricity-carbon analysis model demonstrate the proposed method can assess the protection effectiveness of differential privacy algorithms effectively. Ablation experiments further show that the formalization link plays a key role in the discrimination of the algorithm.

关键词

Keywords

references

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