Skip to main content
Log in

Upkeeping secrecy in information extraction using ‘k’ division graph based postulates

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

The prevailing mechanisms for extracting useful information might offer enhanced results in extraction of useful data for creating classification policies. The goal is to administer the disputes prevailing within the categorization for supervised data. Moreover several schemes conceal the individuality of the schemes employed which attempts to conceal the location of information which might become a serious issue during conserving privacy of the data stored. The aim is to address the disputes by making use of a graph and hypothetical based scheme termed as k-segmentation of graphs which delivers the creation of difficult choice based tree classification organized into a priority based hierarchy. The analysis depicts that the designed scheme offers accuracy and effectiveness.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Niewolny, D.: How the Internet of Things is revolutionizing healthcare. Tech. Rep, FreeScale Technologies (Whitepaper) (2010)

  2. Kim, J.T.: Privacy and security issues for healthcare system with embedded RFID system on Internet of Things. Adv. Sci. Technol. Lett. 72, 109–112 (2014)

    Article  Google Scholar 

  3. Will the Internet of Things Analytics Revolutionize the Healthcare Industry? Tech. Rep. Saviance Technologies (2009)

  4. Agrawal, R., Srikant, R.: Privacy preserving data mining. In: ACM SIGMOD International Conference on Management of data (2000)

  5. Vaidya, J., Clifton, C.: Privacy-preserving data mining: why, how, and when. IEEE Secur. Priv. 2(6), 19–27 (2007)

    Article  Google Scholar 

  6. Quinlan, J.R.: Induction of Decision Trees. Mach. Learn. 1, 569–571 (1986)

    Google Scholar 

  7. Smys, S., Bala, GJ., Raj, JS.: Construction of virtual backbone to support mobility in MANET—a less overhead approach. In: Application of Information and Communication Technologies, AICT 2009. International Conference on 2009, pp. 1–4. IEEE (2009)

  8. Aggarwal, C.C., Yu, P.S.: A course in number theory. Privacy Preserving Data Mining: Models and Algorithms. Springer, New York (2010)

  9. Wang, P.: Survey on privacy preserving data mining. Int. J. Digit. Content. Technol. Appl. 4(9) (2010)

  10. Kou, G., Peng, Y., Shi, Y., Chen, Z.: Privacy-preserving data mining of medical fata using data separation based techniques. Data Sci. J 6, S429–S434 (2007)

    Article  Google Scholar 

  11. Abad, B., Kinariwala, S.A.: A novel approach for privacy preserving in medical data mining using sensitivity based anonymity. Int. J. Comput. Appl. 42(4), 13–16 (2012)

    Google Scholar 

  12. Wong, R., Li, J., Fu, A., Wang, K.: (, k)-Anonymity: an enhanced k-anonymity model for privacy preserving data publishing, pp. 754–759. KDD, Japan (2006)

  13. Wu, X.: Information security in big data: privacy and data mining. IEEE Access 2, 1149–1176 (2011)

    Google Scholar 

  14. Paryasto, M., Alamsyah, A., Rahardjo, B., Kuspriyanto : Big-data security management issues. In: 2nd International Conference on Information and Communication Technology (2014)

  15. Tripathy, A., Pradhan, M.: A novel framework for preserving privacy of data using correlation analysis. In: International Conference on Advances in Computing Communications and Informatics–ICACCI 12, (2012)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to B. Santhosh Kumar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, B.S., Karthik, S. & Arunachalam, V.P. Upkeeping secrecy in information extraction using ‘k’ division graph based postulates. Cluster Comput 22 (Suppl 1), 57–63 (2019). https://doi.org/10.1007/s10586-018-1705-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-1705-2

Keywords

Navigation