面向时序数据发布的隐私保护方法研究

doi:10.11959/j.issn.1000-436x.2015305

通信学报 ›› 2015, Vol. 36 ›› Issue (Z1): 243-249.doi: 10.11959/j.issn.1000-436x.2015305

面向时序数据发布的隐私保护方法研究

于东,康海燕

北京信息科技大学信息管理学院，北京 100192

出版日期:2015-11-25 发布日期:2015-12-29
基金资助:
北京市社会科学基金资助项目;北京市优秀人才培养基金资助项目;国家自然科学基金资助项目;教育部人文社会科学青年基金资助项目

Privacy protection method on time-series data publication

Dong YU,Hai-yan KANG

School of Information Management，Beijing Information Science and Technology University，Beijing 100192，China

Online:2015-11-25 Published:2015-12-29
Supported by:
The Social Science Foundation of Beijing;The Excellent Talents Program of Beijing;The National Natural Science Foundation of China;Humanity and Social Science Youth Foun-dation of Ministry of Education

摘要/Abstract

摘要：

针对动态数据（时序数据）提出一种抽样过滤技术的差分隐私保护模型及评价机制。首先，利用固定抽样法对原始时序数据进行抽样，非抽样数据直接发布；其次，对抽样数据采取差分隐私保护机制进行加噪；然后，运用Kalman过滤技术对保护后的抽样数据进行预测修正；最后，通过互信息评价机制对不同抽样间隔下的数据进行评价。通过实验证明抽样过滤机制在安全性和实用性上达到最优的平衡性。

关键词: 差分隐私, 时序数据, 数据发布, 抽样, Kalman过滤, 互信息

Abstract:

A differential privacy model was proposed based on the sampling filtering and the mechanism of evaluation.Firstly,fixed sampling method was used to sample the original data and the non-sampling data be published directly.Secondly,for the sampling date,utilize the differential privacy mechanism to add the noise.Then,use Kalman to correct the sampling date.Finally,use the mutual information to evaluate data under different sampling intervals.Through the experiment,it is proved that the mechanism can achieve a good balance between the practicality and protective.

Key words: differential privacy, time-series data, data publication, sample, Kalman filter, mutual information

于东,康海燕. 面向时序数据发布的隐私保护方法研究[J]. 通信学报, 2015, 36(Z1): 243-249.

Dong YU,Hai-yan KANG. Privacy protection method on time-series data publication[J]. Journal on Communications, 2015, 36(Z1): 243-249.

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面向时序数据发布的隐私保护方法研究

Privacy protection method on time-series data publication

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