通信学报 ›› 2019, Vol. 40 ›› Issue (6): 74-81.doi: 10.11959/j.issn.1000-436x.2019088

• 学术论文 • 上一篇    

随机响应机制效用优化研究

周异辉1,鲁来凤2,3(),吴振强1,3   

  1. 1 陕西师范大学计算机科学学院,陕西 西安 710119
    2 陕西师范大学数学与信息科学学院,陕西 西安 710119
    3 贵州大学贵州省公共大数据重点实验室,贵州 贵阳 550025
  • 修回日期:2019-02-13 出版日期:2019-06-01 发布日期:2019-07-04
  • 作者简介:周异辉(1981- ),女,河北蠡县人,博士,陕西师范大学讲师,主要研究方向为网络安全、大数据环境下的隐私保护和差分隐私。|鲁来凤(1979- ),女,安徽桐城人,博士,陕西师范大学副教授,主要研究方向为网络安全、大数据环境下的隐私保护和差分隐私。|吴振强(1968- ),男,陕西柞水人,博士,陕西师范大学教授、博士生导师,主要研究方向为网络数据科学、纳米网络、分布式计算、隐私保护、可信计算等。
  • 基金资助:
    国家自然科学基金资助项目(61673251);陕西省自然科学基金资助项目(2018JM6050);陕西省自然科学基金资助项目(2017JQ6038);贵州省公共大数据重点实验室开放课题基金资助项目(2017BDKFJJ026);贵州省公共大数据重点实验室开放课题基金资助项目(2018BDKFJJ004);中央高校基本科研业务费专项基金资助项目(GK201903091);中央高校基本科研业务费专项基金资助项目(GK201903011)

Study on utility optimization for randomized response mechanism

ZHOU Yihui1,LU Laifeng2,3(),WU Zhenqiang1,3   

  1. 1 School of Computer Science,Shaanxi Normal University,Xi’an 710119,China
    2 School of Mathematics and Information Science,Shaanxi Normal University,Xi’an 710119,China
    3 Guizhou Provincial Key Lab of Public Big Data,Guizhou University,Guiyang 550025,China
  • Revised:2019-02-13 Online:2019-06-01 Published:2019-07-04
  • Supported by:
    The National Natural Science Foundation of China(61673251);The Natural Science Foundation of Shaanxi Province(2018JM6050);The Natural Science Foundation of Shaanxi Province(2017JQ6038);The Open Project Fund of Guizhou Provincial Key Laboratory of Public Big Data(2017BDKFJJ026);The Open Project Fund of Guizhou Provincial Key Laboratory of Public Big Data(2018BDKFJJ004);The Fundamental Research Funds for the Central Universities(GK201903091);The Fundamental Research Funds for the Central Universities(GK201903011)

摘要:

针对本地化差分隐私中的隐私-效用均衡问题,对差分隐私和近似差分隐私情形下的二元广义随机响应机制建立效用优化模型,并采用图解法、最优性证明、软件求解和极值点等方法求解,得到了效用最优值与隐私预算、输入数据分布的精确表达式,给出了相应的效用最优机制。研究结果表明效用最优值和效用最优机制均与隐私预算和输入数据分布相关。另外,多元随机响应机制效用优化模型可通过本地化差分隐私极值点来求解。

关键词: 本地化差分隐私, 随机响应, 效用优化, 极值点, 单纯形法

Abstract:

For the study of privacy-utility trade-off in local differential privacy,the utility optimization models of binary generalized random response mechanism for the case of differential privacy and approximate differential privacy were established.By graphic method,optimality proof,software solution and extreme point method,the exact expression of the optimal utility with privacy budget and the distribution of input data was obtained,and the corresponding optimal randomized response mechanism was given.The results show that both the optimal utility and optimal mechanism are related to privacy budget and input data distribution.Moreover,the discussion for multivariate randomized response mechanism shows that the method of extreme points of local differential privacy is feasible to the solution.

Key words: local differential privacy, randomized response, utility optimization, extreme point, simplex method

中图分类号: 

  • TP309