基于置信度分析的差分隐私保护参数配置方法研究

李森有; 季新生; 游伟

doi:10.11959/j.issn.2096-109x.2019036

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基于置信度分析的差分隐私保护参数配置方法研究

专栏：隐私保护关键技术及其新型应用模式探索 | 更新时间：2024-06-03

- 基于置信度分析的差分隐私保护参数配置方法研究
- Research on differential privacy protection parameter configuration method based on confidence level
- 网络与信息安全学报 2019年5卷第4期页码：29-39
- 作者机构：
- 作者简介：
  
  [ "李森有（1993- ），河南驻马店人，国家数字交换系统工程技术研究中心硕士生，主要研究方向为新一代移动通信技术、移动通信安全、隐私保护。" ]
  [ "季新生（1968- ），男，河南驻马店人，国家数字交换系统工程技术研究中心教授、博士生导师，主要研究方向为网络空间安全、移动通信网络安全、拟态安全。" ]
  [ "游伟（1984- ），男，山东青岛人，国家数字交换系统工程技术研究中心讲师，主要研究方向为新一代移动通信网络技术、移动通信网络安全。" ]
- 基金信息：
  
  国家自然科学基金创新研究群体基金资助项目;The Foundation for Innovative Research Groups of the National Natural Science Foundation of China(61521003);国家自然科学基金创新研究群体资助项目;The National Natural Science Foundation for Creative Research Groups of China(61801515);国家重点研发计划基金资助项目;The National Key R＆D Program of China(2016YFB0801605)
- DOI：10.11959/j.issn.2096-109x.2019036
  中图分类号： TP309.2
- 网络首发：2019-08，
  
  纸质出版：2019-08-15
- 稿件说明：
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李森有, 季新生, 游伟. 基于置信度分析的差分隐私保护参数配置方法研究[J]. 网络与信息安全学报, 2019,5(4):29-39.

Senyou LI, Xinsheng JI, Wei YOU. Research on differential privacy protection parameter configuration method based on confidence level[J]. Chinese Journal of Network and Information Security, 2019, 5(4): 29-39.
李森有, 季新生, 游伟. 基于置信度分析的差分隐私保护参数配置方法研究[J]. 网络与信息安全学报, 2019,5(4):29-39. DOI： 10.11959/j.issn.2096-109x.2019036.

Senyou LI, Xinsheng JI, Wei YOU. Research on differential privacy protection parameter configuration method based on confidence level[J]. Chinese Journal of Network and Information Security, 2019, 5(4): 29-39. DOI： 10.11959/j.issn.2096-109x.2019036.

摘要

为了解决数据发布和分析过程中用户真实数据信息被披露的问题，降低攻击者通过差分攻击和概率推理攻击获取真实结果的概率，提出了一种基于置信度分析的差分隐私保护参数配置方法。在攻击者概率推理攻击模型下对攻击者置信度进行分析，使之不高于根据数据隐私属性所设置的隐私概率阈值。所提出的方法能够针对不同查询用户查询权限的差异配置更加合理的隐私保护参数，避免了隐私披露的风险。实验分析表明，所提出的方法根据查询权限、噪声分布特性以及数据隐私属性分析攻击者置信度与隐私保护参数的对应关系，并据此推导出隐私保护参数的配置公式，从而在不违背隐私保护概率阈值的情况下配置合适的ε参数。

Abstract

In order to solve the problem that the user's real data information is disclosed during the data release and analysis process

and reduce the probability of an attacker gaining real results through differential attacks and probabilistic inference attacks

a differential privacy protection parameter configuration method based on confidence level is proposed.Analysis of attacker confidence under attacker probabilistic inference attack model and make it no higher than the privacy probability threshold set according to the data privacy attribute.The proposed method can configure more reasonable privacy protection parameters for different query privilege of query users

and avoids the risk of privacy disclosure.The experimental analysis shows that the proposed method analyzes the correspondence between attacker confidence level and privacy protection parameters based on query privilege

noise distribution characteristics and data privacy attributes

and derives the configuration formula of privacy protection parameters

which configure the appropriate parameters without violating the privacy protection probability threshold.

关键词

Keywords

references

LIANG F , YU W , AN D , et al . A survey on big data market:pricing,trading and protection [J ] . IEEE Access , 2018 , 6 : 15132 - 15154 .

INAN A , GURSOY M E , SAYGIN Y . Sensitivity analysis for non-interactive differential privacy:bounds and efficient algorithms [J ] . IEEE Transactions on Dependable ＆ Secure Computing , 2017 ,( 99 ): 1 - 1 .

LATANYA S . k-anonymity:a model for protecting privacy [J ] . International Journal on Uncertainty,Fuzziness and Knowledge based Systems , 2002 , 10 ( 5 ): 557 - 570 .

ASHWIN M , JOHANNES G , DANIEL K . ℓ-diversity:privacy beyond k-anonymity [C ] . International Conference on Data Engineering , 2006 , 1 ( 1 ): 24 - 35 .

LI N H , LI T C , SURESH V . T-closeness:privacy beyond k-anonymity and l-diversity [C ] . IEEE International Conference on Data Engineering , 2007 : 106 - 115 .

DWORK C , ROTH A . The algorithmic foundations of differential privacy [M ] . Now Publishers Inc . 2014 .

ZHU T Q , LI G , ZHOU W , et al . Differentially private data publishing and analysis:a survey [J ] . IEEE Transactions on Knowledge＆ Data Engineering , 2017 , 29 ( 8 ): 1619 - 1638 .

DWORK C . Calibrating noise to sensitivity in private data analysis [J ] . Lecture Notes in Computer Science , 2012 , 3876 ( 8 ): 265 - 284 .

熊平 , 朱天清 , 王晓峰 . 差分隐私保护及其应用 [J ] . 计算机学报 , 2014 , 37 ( 1 ): 101 - 122 .

XIONG P , ZHU T Q , WANG X F . A survey on differential privacy protection and application [J ] . Chinese Journal of Computers , 2014 , 37 ( 1 ): 101 - 122 .

LI Q , LI Y , ZENG G , et al . Differential privacy data publishing method based on cell merging [C ] // IEEE International Conference on Networking,Sensing and Control . 2017 : 778 - 782 .

SORUIA-COMAS J , DOMINGO-FERRER J , SANCHEZ D , et al . Individual differential privacy:a utility-preserving formulation of differential privacy guarantees [J ] . IEEE Transactions on Information Forensics ＆ Security , 2017 , 12 ( 6 ): 1418 - 1429 .

SMITH M , LOPEZ MAA , ZWIESSELE M , et al . Differentially private regression with Gaussian processes [C ] // International Conference on Artificial Intelligence and Statistics . 2018 .

HSU J , GABOARDI M , HAEBERLEN A , et al . Differential privacy:an economic method for choosing epsilon [C ] // 2014 IEEE 27th Computer Security Foundations Symposium . 2014 : 398 - 410 .

LEE J , CLIFTON C . How much is enough? choosing ε for differential privacy [C ] // International Conference on Information Security . 2011 : 325 - 340 .

何贤芒 , 王晓阳 , 陈华辉 . 差分隐私保护参数 ε 的选取研究 [J ] . 通信学报 , 2015 , 36 ( 12 ): 124 - 130 .

HE X M , WANG X Y , CHEN H H . Study on choosing the parameter ε in differential privacy [J ] . Journal on Communications , 2015 , 36 ( 12 ): 124 - 130 .

郝晨艳 , 彭长根 , 张盼盼 . 重复攻击下差分隐私保护参数ε的选取方法 [J ] . 计算机工程 , 2018 , 44 ( 7 ): 151 - 155 .

HAO Y C , PENG C G , ZHANG P P . Selection method of differential privacy protection parameter ε under repeated attack [J ] . Computer Engineering , 2018 , 44 ( 7 ): 151 - 155 .

DWORK C . Differential privacy [J ] . Encyclopedia of Cryptography and Security , 2011 : 338 - 340 .

DWORK C , ROTH A . The algorithmic foundations of differential privacy [J ] . Foundations and Trends in Theoretical Computer Science , 2014 , 9 ( 3-4 ): 211 - 407 .

BONOMI L , XIONG L . A two-phase algorithm for mining sequential patterns with differential privacy [C ] // The 22nd ACM International Conference on Information ＆ Knowledge Management . 2013 : 269 - 278 .

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