Privacy-preserving publishing for streaming data

被引：0

作者：

Huang, Xuezhen ^{[1
]}

Liu, Jiqiang ^{[1
]}

Han, Zhen ^{[1
]}

Yang, Jun ^{[1
]}

机构：

[1] School of Computer and Information Technology, Beijing Jiaotong University, Beijing

来源：

Journal of Computational Information Systems | 2015年 / 11卷 / 05期

关键词：

Anonymity; Data security; Privacy protection; Streaming data;

D O I：

10.12733/jcis13591

中图分类号：

学科分类号：

摘要：

Privacy-preserving data publishing (PPDP) is one of the hot issues in the field of the information security. The previous PPDP technique can not deal with generality attack, which explicitly contains sensitivity attack and similarity attack. We recently proposed a novel model to avoid generality attack on the texture of static data. In this paper, we propose a new anonymity model for streaming data which can reduce the possibility of the suffering from generality attack. We prove that the optimal anonymity problem is NP-hard and direct an algorithm (TDLW) for the model. Our experiments validate the improvement of our model with real data. Copyright © 2015 Binary Information Press.

引用

页码：1863 / 1877

页数：14

共 25 条

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