Privacy protection method on time-series data publication

Dong YU; Hai-yan KANG

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

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Privacy protection method on time-series data publication

Academic communication | 更新时间：2024-06-05

- Privacy protection method on time-series data publication
- Journal on Communications Vol. 36, Issue Z1, Pages: 243-249(2015)
- 作者机构：
  
  北京信息科技大学信息管理学院，北京 100192
- 作者简介：
- 基金信息：
  
  The Social Science Foundation of Beijing(15JGB099);The Excellent Talents Program of Beijing(2013E005007000001);The National Natural Science Foundation of China(61370139);Humanity and Social Science Youth Foun-dation of Ministry of Education(11YJC870011)
- DOI：10.11959/j.issn.1000-436x.2015305
  CLC： TP309
- Online First：2015-11，
  
  Published：25 November 2015
- 稿件说明：
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Dong YU, Hai-yan KANG. Privacy protection method on time-series data publication[J]. Journal on Communications, 2015, 36(Z1): 243-249.
DOI：

Dong YU, Hai-yan KANG. Privacy protection method on time-series data publication[J]. Journal on Communications, 2015, 36(Z1): 243-249. DOI： 10.11959/j.issn.1000-436x.2015305.

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。

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.

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Keywords

references

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