Towards Efficient Privacy Preserving Big Data using Anonymization & Differential Privacy

  • Sandeep Kour


Privacy preserving issues in big data have emerged globally. A huge amount of microdata is collected
and published daily which demands for its privacy so as not to disclose the sensitive information.
Various data mining tasks are carried over this microdata collected from centralized servers for
getting useful information, analysis purposes and decision making. There are many factors that lead
to a conflict between privacy and utility of data and performing mining tasks on such a large data
often results in information loss and privacy violation. Despite of the storage and processing
challenge of big data, preserving this massive data is a major challenge in order to gain the trust of
users. So, there is a need for an efficient privacy technique which does not result in information loss
and also maintains the data quality for further classification and analysis purposes. This paper
reviews some existing challenges of privacy in big data and gives a concise and a systematic review of
existing techniques of privacy preserving: Anonymization and Differential Privacy and then finally
proposed an efficient hybrid technique towards the privacy preserving so as to have less information
loss and attain data utility

How to Cite
Sandeep Kour. (2020). Towards Efficient Privacy Preserving Big Data using Anonymization & Differential Privacy. International Journal of Advanced Science and Technology, 29(10s), 2566-2575. Retrieved from