Information Security and Data Mining in Big Data

Authors(2) :-Tejas P. Adhau, Dr. Mahendra A. Pund

The growing popularity and development of data mining technologies bring a serious threat to the security of individual's sensitive information. An emerging research topic in data mining, known as privacy-preserving data mining (PPDM), has been extensively studied in recent years. The basic idea of PPDM is to modify the data in such a way so as to perform data mining algorithms effectively without compromising the security of sensitive information contained in the data. Current studies of PPDM mainly focus on how to reduce the privacy risk brought by data mining operations, while in fact, unwanted disclosure of sensitive information may also happen in the process of data collecting, data publishing, and information (i.e., the data mining results) delivering. In this paper, we view the privacy issues related to data mining from a wider perspective and investigate various approaches that can help to protect sensitive information. In particular, we identify four different types of users involved in data mining applications, namely, data provider, data collector, data miner, and decision maker. For each type of user, we focus on his privacy and how to protect sensitive information.

Authors and Affiliations

Tejas P. Adhau
Department of Computer Science of Engineering/SGBAU University/PRMIT Badnera/Amravati, Maharashtra, India
Dr. Mahendra A. Pund
Department of Computer Science of Engineering/SGBAU University/PRMIT Badnera/Amravati, Maharashtra, India

Data mining, sensitive information, privacy-preserving data mining provenance, anonymization , privacy auction, antitracking.

  1. L. BrankovicRE and V. Estivill-Castro, "Privacy issues in knowledge discovery and data mining," in Proc. Austral. Inst.Comput. Ethics Conf., 1999, pp. 89–99.
  2. C. C. Aggarwal and S. Y. Philip, A General Survey of PrivacyPreserving Data Mining Models and Algorithms. New York,NY, USA: Springer-Verlag, 2008.
  3. L. Sweeney, "k-anonymity: A model for protecting privacy," Int. J. Uncertainty, Fuzziness Knowl.-Based Syst., vol. 10, no. 5,pp. 557–570, 2002.
  4. Verizon Communications Inc. (2013). 2013 Data Breach Investigations Report. Online]. Available:
  5. Lei Xu; Chunxiao Jiang; Jian Wang; Jian Yuan; Yong Ren, "Information Security in Big Data: Privacy and Data Mining,"Access, IEEE , vol.2, no., pp.1149,1176, 2014
  6. R. C.-W. Wong and A. W.-C. Fu, "Privacy-preserving data publishing: An overview," Synthesis Lectures Data Manage.,vol. 2, no. 1, pp. 1–138, 2010.
  7. J. Xu, W. Wang, J. Pei, X. Wang, B. Shi, and A. W.-C. Fu, "Utility-based anonymization for privacy preservation with less information loss," ACM SIGKDD Explorations Newslett., vol. 8, no. 2, pp. 21–30, 2006.
  8. R. Gibbons, A Primer in Game Theory. Hertfordshire, U.K.: Harvester Wheatsheaf, 1992.
  9. Tene and J. Polenetsky, "To track or ‘do not track’: Advancing transparency and individual control in online behavioral advertising," Minnesota J. Law, Sci. Technol., no. 1, pp. 281–357, 2012.
  10. M. B. Malik, M. A. Ghazi, and R. Ali, "Privacy preserving data mining techniques: Current scenario and future prospects,"in Proc. 3rd Int. Conf. Comput. Commun. Technol. (ICCCT), Nov. 2012, pp. 26–32.
  11. S. Matwin, "Privacy-preserving data mining techniques: Survey and challenges," in Discrimination and Privacy in the Information Society. Berlin, Germany: Springer-Verlag, 2013, pp. 209–221.
  12. E. Rasmusen, Games and Information: An Introduction to Game Theory, vol. 2. Cambridge, MA, USA: Blackwell, 1994.
  13. V. Ciriani, S. De Capitani di Vimercati, S. Foresti, and P. Samarati, "Microdata protection," in Secure Data Management in Decentralized Systems. New York, NY, USA: Springer-Verlag, 2007, pp. 291–321.
  14. D. C. Parkes, "Iterative combinatorial auctions: Achieving economic and computational efficiency," Ph.D. dissertation,Univ. Pennsylvania, Philadelphia, PA, USA, 2001.

Publication Details

Published in : Volume 3 | Issue 2 | March-April 2017
Date of Publication : 2017-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 661-673
Manuscript Number : IJSRSET1732191
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Tejas P. Adhau, Dr. Mahendra A. Pund, " Information Security and Data Mining in Big Data, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 3, Issue 2, pp.661-673, March-April-2017.
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