An Approach for Distributing Sensitive Values in k-Anonymity

Widodo, Eko K. Budiardjo, Wahyu C. Wibowo, Harry T.Y. Achsan

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

Abstract

k-anonymity is a popular model in privacy preserving data publishing. It provides privacy guarantee when a microdata table is released. In microdata, sensitive attributes contain high-sensitive and low sensitive values. Unfortunately, study in anonymity for distributing sensitive value is still rare. This study aims to distribute evenly high-sensitive value to quasi identifier group. We proposed an approach called Simple Distribution of Sensitive Value. We compared our method with systematic clustering which is considered as very effective method to group quasi identifier. Information entropy is used to measure the diversity in each quasi identifier group and in a microdata table. Experiment result show our method outperformed systematic clustering when high-sensitive value is distributed.

Original languageEnglish
Title of host publication2019 International Workshop on Big Data and Information Security, IWBIS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages109-114
Number of pages6
ISBN (Electronic)9781728153476
DOIs
Publication statusPublished - Oct 2019
Event2019 International Workshop on Big Data and Information Security, IWBIS 2019 - Bali, Indonesia
Duration: 11 Oct 2019 → …

Publication series

Name2019 International Workshop on Big Data and Information Security, IWBIS 2019

Conference

Conference2019 International Workshop on Big Data and Information Security, IWBIS 2019
CountryIndonesia
CityBali
Period11/10/19 → …

Keywords

  • high-sensitive value
  • k-anonymity
  • SDSV
  • simple distribution of sensitive values

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    Widodo, Budiardjo, E. K., Wibowo, W. C., & Achsan, H. T. Y. (2019). An Approach for Distributing Sensitive Values in k-Anonymity. In 2019 International Workshop on Big Data and Information Security, IWBIS 2019 (pp. 109-114). [8935849] (2019 International Workshop on Big Data and Information Security, IWBIS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWBIS.2019.8935849