A Comparative Study of Data Anonymization Techniques

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In today's digital era, it is a very common practice for organizations to collect data from individual users. The collected data is then stored in multiple databases which contain personally identifiable information (PII). This may lead to a major source of privacy risk for the database. Various privacy preservation techniques have been proposed such as perturbation, anonymization and cryptographic. In this study, five anonymization techniques are compared using the same dataset. In addition to that, this study reviews the strengths and weaknesses of the different technique. In the evaluation of efficiency, suppression is found as the most efficient while swapping is in the last place. It is also revealed that swapping is the most resource-consuming technique while suppressing being less resource consuming.

Original languageEnglish
Title of host publicationProceedings - 5th IEEE International Conference on Big Data Security on Cloud, BigDataSecurity 2019, 5th IEEE International Conference on High Performance and Smart Computing, HPSC 2019 and 4th IEEE International Conference on Intelligent Data and Security, IDS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages306-309
Number of pages4
ISBN (Electronic)9781728100067
DOIs
Publication statusPublished - 01 May 2019
Event5th IEEE International Conference on Big Data Security on Cloud, 5th IEEE International Conference on High Performance and Smart Computing and 4th IEEE International Conference on Intelligent Data and Security, BigDataSecurity/HPSC/IDS 2019 - Washington, United States
Duration: 27 May 201929 May 2019

Publication series

NameProceedings - 5th IEEE International Conference on Big Data Security on Cloud, BigDataSecurity 2019, 5th IEEE International Conference on High Performance and Smart Computing, HPSC 2019 and 4th IEEE International Conference on Intelligent Data and Security, IDS 2019

Conference

Conference5th IEEE International Conference on Big Data Security on Cloud, 5th IEEE International Conference on High Performance and Smart Computing and 4th IEEE International Conference on Intelligent Data and Security, BigDataSecurity/HPSC/IDS 2019
CountryUnited States
CityWashington
Period27/05/1929/05/19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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  • Cite this

    Murthy, S., Abu Bakar, A., Abdul Rahim, F., & Ramli, R. (2019). A Comparative Study of Data Anonymization Techniques. In Proceedings - 5th IEEE International Conference on Big Data Security on Cloud, BigDataSecurity 2019, 5th IEEE International Conference on High Performance and Smart Computing, HPSC 2019 and 4th IEEE International Conference on Intelligent Data and Security, IDS 2019 (pp. 306-309). [8819477] (Proceedings - 5th IEEE International Conference on Big Data Security on Cloud, BigDataSecurity 2019, 5th IEEE International Conference on High Performance and Smart Computing, HPSC 2019 and 4th IEEE International Conference on Intelligent Data and Security, IDS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigDataSecurity-HPSC-IDS.2019.00063