Privacy preserving big data analytics

What is it about?

Big data is large and complex data that is difficult to process by traditional data processing systems. Big data analytics is a process to generate knowledge from large datasets, having variety of data, which is collected from multiple sources, using platforms such as, high performance computing clusters, hadoop, spark, etc. Due to data collection from multiple sources, chances of privacy breach have increased. It is difficult to apply existing privacy models (privacy preserving techniques) in big data analytics because of 3Vs: Volume (large amount of data), Variety (structured, semistructured or unstructured data), and Velocity (fast generation and processing of data), characteristics of big data. This paper discusses about general architecture of big data analytics that shows different stages of big data analytics, which can be helpful to identify the stage, where privacy models can be applied. Based on survey of existing privacy models, a summery has been prepared, that shows relation between privacy models and 3Vs of big data.

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Why is it important?

To know about different existing privacy preserving techniques and how it can be applicable to big data

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