Preservation of privacy of big data using efficient anonymization technique

Afia Naeem, Muhammad Rizwan, Fahad Ahamd

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Big data needs to be retained private because of the increase in the amount of data. Data is generated from social networks, organizations and various other ways, which is known as big data. Big data requires large storage as well as high computational power. At every stage, the data needs to be protected. Privacy preservation plays an important role in keeping sensitive information protected and private from any attack. Data anonymization is one of the techniques to anonymize data to keep it private and protected, which includes suppression, generalization, and bucketization. It keeps personal and private data anonymous from being known by others. But when it is implemented on big data, these techniques cause a great loss of information and also fail in defense of the privacy of big data. Moreover, for the scenario of big data, the anonymization should not only focus on hiding but also on other aspects. This paper aims to provide a technique that uses slicing, suppression, and functional encryption together to achieve better privacy of big data with data anonymization.
Original languageEnglish
Pages (from-to)14-22
Number of pages9
JournalLahore Garrison University Research Journal of Computer Science and Information Technology
Issue number4
Publication statusPublished - 25 Sept 2020


  • Big Data
  • Anonymization
  • slicing
  • functional encryption
  • Privacy Preservation

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