Concealment Conserving the Data Mining of Groups & Individual

  • Abu Sarwar Zamani Lecturer, Dept. of Computer Science, College of Science & Humanity, Al Quwaiyah, Shaqra University, Kingdom of Saudi Arabia
  • Md. Mobin Akhtar Lecturer, College of Computing and Information Technology,Shaqra University, Kingdom of Saudi Arabia
  • Danish Ahamad College of Science and Arts, Sajir, Shaqra University, Kingdom of Saudi Arabia
Keywords: Traditional Datasets, Mobility Data.


We present an overview of privacy preserving data mining, one of the most popular directions in the data mining research community. In the first part of the chapter, we presented approaches that have been proposed for the protection of either the sensitive data itself in the course of data mining or the sensitive data mining results, in the context of traditional (relational) datasets. Following that, in the second part of the chapter, we focused our attention on one of the most recent as well as prominent directions in privacy preserving data mining: the mining of user mobility data. Although still in its infancy, privacy preserving data mining of mobility data has attracted a lot of research attention and already counts a number of methodologies both with respect to sensitive data protection and to sensitive knowledge hiding. Finally, in the end of the chapter, we provided some roadmap along the field of privacy preserving mobility data mining as well as the area of privacy preserving data mining at large.


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How to Cite
Zamani, A. S., Akhtar, M. M., & Ahamad, D. (2018). Concealment Conserving the Data Mining of Groups & Individual. Journal of Information Sciences and Computing Technologies, 7(1), 648-653. Retrieved from