大数据
计算机科学
多样性(控制论)
信息隐私
互联网隐私
相关性(法律)
数据科学
设计隐私
透视图(图形)
社会化媒体
隐私保护
比例(比率)
隐私软件
价值(数学)
数据匿名化
计算机安全
万维网
数据挖掘
政治学
物理
量子力学
法学
机器学习
人工智能
作者
Poornima Kulkarni,N. K. Cauvery
标识
DOI:10.1109/icccs51487.2021.9776332
摘要
The evolution of social media has made big data more and more accessible to the public along with the availability of diverse datasets which may lead to the rise of privacy concerns. These datasets may contain Personally Identifiable Information which is meant for a specific purpose can lead to the violation of the user's privacy if misused. The existing data protection mechanisms don't scale up with the characteristics of big data namely Volume, Velocity, Variety, Veracity, and Value and therefore there is a need to redefine privacy-preserving techniques to address the issues related to the characteristics that are associated with big data. In this work, we assess and analyze the capability of traditional privacy-preserving techniques and the relevance of these techniques in the present scenario.
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