Data Anonymization Techniques for Preserving Privacy in Public Release Data Model: A Technical Review

Authors

  • Arun Amaithi Rajan National University of Singapore, Singapore
  • Anitha Amaithi Rajan Francis Xavier Engineering College, Tirunelveli, India

Keywords:

Anonymization techniques, privacy-preserving algorithm, synthetic data generation, pseudonymization technique

Abstract

The protection of sensitive records is very necessary for a modern scenario. Lately, the informational index is accessible for open use for statistical analysis. In this situation increasingly sensitive information like medical records, nation resident`s data, worker`s compensation data and so on are affecting to a higher extent since we are giving our data to people in general. Thus, Data anonymization assumes significance in the present day to protect the open discharge of sensitive information. In this paper, we reviewed some anonymization techniques and proposed a simple anonymization technique which is the combination of synthetic data generation and pseudonymization approach which reduces attacks on sensitive facts.

 

References

H. Kargupta, S. Datta, Q.Wang, and K. Sivakumar, “On the Privacy Preserving Properties of Random Data Perturbation Techniques,” In Proceedings of the International Conference on Data Mining (ICDM), pp. 99-106, 2003.

Kavita Rodiya and Parmeet Gill, “A Review on Anonymization Techniques for privacy preserving data publishing,” IJRET: International Journal of Research in Engineering and Technology, November 2015.

Disha Dubli and D.K Yadav, “Secure Techniques of Data Anonymization for Privacy Preservation,” International Journal of Advanced Research in Computer Science, Vol. 8, Issue. 05, pp. 1694-1698, 2017.

Surendra .H, Dr. Mohan .H .S, “A Review of Synthetic Data Generation Methods for Privacy Preserving Data Publishing,” International Journal of Scientific & Technology Research, Vol. 6, Issue. 03, pp. 95-101, March 2017.

White Paper on Pseudonymization Drafted by the Data Protection Focus Group for the Safety, Protection, and Trust Platform for Society and Businesses in Connection with the 2017 Digital Summit.

Ajayi, Olusola Olajide, Adebiyi, Temidayo Olarewaju, “Application of Data Masking in Achieving Information Privacy,” IOSR Journal of Engineering (IOSRJEN), Vol. 04, Issue. 02, pp. 13-21, 2014.

Thakkar A., Bhatti A.A., Vasa J., “Correlation Based Anonymization Using Generalization and Suppression for Disclosure Problems,” In Advances in Intelligent Informatics. Advances in Intelligent Systems and Computing, Vol. 320, pp. 581-592, 2015.

Yang Xu, Tinghuai Ma, Meili Tang and Wei Tian, “A Survey of Privacy Preserving Data Publishing using Generalization and Suppression,” Applied Mathematics & Information Sciences. Vol. 8, Issue. 03, pp. 1103-1116, 2014.

Anisha Tiwari1, Minu Choudhary, “A Review on K-Anonymization Techniques,” Scholars Journal of Engineering and Technology (SJET), Vol. 5, Issue. 06, pp. 238-245, 2017.

Tanashri Karle and Prof Deepali Vora, “Privacy Preservation in Big Data Using Anonymization Techniques,” In International Conference on Data Management Analytics and Innovation (ICDMAI), pp. 340-343, 2017.

Ramesh Bandaru , Rao S Basavala "Information Leakage through Social Networking Websites leads to Lack of Privacy and Identity Theft Security Issues." International Journal of Scientific Research in Computer Science and Engineering (IJSRCSE), Vol 1, Issue. 03, pp. 1-7, 2013.

Downloads

Published

2020-02-28

How to Cite

[1]
A. A. Rajan and A. A. Rajan, “Data Anonymization Techniques for Preserving Privacy in Public Release Data Model: A Technical Review”, Int. J. Sci. Res. Comp. Sci. Eng., vol. 8, no. 1, pp. 58–62, Feb. 2020.

Issue

Vol. 8 No. 1 (2020): February

Section

Review Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.