Double Privacy Layer Architecture for Big Data Framework
Double Privacy Layer Architecture for Big Data Framework
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DOI：
10.14257/ijseia.2016.10.2.22
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发表时间：
2016-02
期刊：
影响因子：
--
通讯作者：
Do-Eun Cho;Si-Jung Kim;Sang-Soo Yeo
中科院分区：
文献类型：
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作者：
Do-Eun Cho;Si-Jung Kim;Sang-Soo Yeo
大数据是一种新兴的、非常重要的技术，可以高效、有效地收集和分析大量实时产生的数据，但它也存在大量敏感数据，导致隐私受到侵犯。大数据分析可以为我们提供非常定制且有效的分析结果，但该技术可能被滥用于侵犯个人用户的隐私。本文介绍了在数据收集阶段、数据分析阶段和呈现服务阶段可以收集和/或合成的敏感信息。然后本文提出了一种保护用户隐私的双层架构。所提出的架构提供了从大数据处理数据库中屏蔽敏感信息的两个步骤，本文通过一些示例详细介绍了这些步骤。
Big data is an emerging and very considerable technology for gathering and analyzing a huge volume of real-time produced data efficiently and effectively, but it has also a great volume of sensitive data arising invasion of privacy. Big data analyses can give us very customized and effective analysis results, but this technology can be abused for privacy invasion of personal users. This paper introduces sensitive information which can be collected and/or synthesized at the data collection stage, data analysis stage, and presentation service stage. And then this paper proposes a double layered architecture for preserving user privacy. The proposed architecture provides two steps for masking sensitive information from the big data processing databases, and these steps are presented in detail with some examples in this paper.