Shifting Dataset to Preserve Data Privacy

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

Abstract

Data analytic is very valuable in any domain that produces large amount of data making demands on full datasets to be revealed for analytic purposes are rising. Regardless, the privacy of the released dataset should be preserved. New techniques using synthetic data as a mean to preserve the privacy has been identified as appropriate approach to fulfill the demand. In this paper, a privacy-preserving data synthetic framework for data analytic is proposed. Using a generative model that captures the density function of data attributes, the privacy-preserving synthetic data is produced. We performed classification task through various machine learning classifiers in measuring the data utility of the new privacy-preserving synthesized data.

Original languageEnglish
Title of host publication2018 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages134-139
Number of pages6
ISBN (Electronic)9781538672631
DOIs
Publication statusPublished - 31 Jan 2019
Event2018 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2018 - Langkawi, Malaysia
Duration: 21 Nov 201822 Nov 2018

Publication series

Name2018 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2018

Conference

Conference2018 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2018
CountryMalaysia
CityLangkawi
Period21/11/1822/11/18

Fingerprint

Data privacy
Probability density function
privacy
Learning systems
Classifiers
Privacy
Privacy preserving
demand

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems and Management
  • Education
  • Communication

Cite this

Pozi, M. S. M., Abu Bakar, A., Ismail, R., Yussof, S., Abdul Rahim, F., & Ramli, R. (2019). Shifting Dataset to Preserve Data Privacy. In 2018 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2018 (pp. 134-139). [8632641] (2018 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IC3e.2018.8632641
Pozi, Muhammad Syafiq Mohd ; Abu Bakar, Asmidar ; Ismail, Roslan ; Yussof, Salman ; Abdul Rahim, Fiza ; Ramli, Ramona. / Shifting Dataset to Preserve Data Privacy. 2018 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2018. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 134-139 (2018 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2018).
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title = "Shifting Dataset to Preserve Data Privacy",
abstract = "Data analytic is very valuable in any domain that produces large amount of data making demands on full datasets to be revealed for analytic purposes are rising. Regardless, the privacy of the released dataset should be preserved. New techniques using synthetic data as a mean to preserve the privacy has been identified as appropriate approach to fulfill the demand. In this paper, a privacy-preserving data synthetic framework for data analytic is proposed. Using a generative model that captures the density function of data attributes, the privacy-preserving synthetic data is produced. We performed classification task through various machine learning classifiers in measuring the data utility of the new privacy-preserving synthesized data.",
author = "Pozi, {Muhammad Syafiq Mohd} and {Abu Bakar}, Asmidar and Roslan Ismail and Salman Yussof and {Abdul Rahim}, Fiza and Ramona Ramli",
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Pozi, MSM, Abu Bakar, A, Ismail, R, Yussof, S, Abdul Rahim, F & Ramli, R 2019, Shifting Dataset to Preserve Data Privacy. in 2018 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2018., 8632641, 2018 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2018, Institute of Electrical and Electronics Engineers Inc., pp. 134-139, 2018 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2018, Langkawi, Malaysia, 21/11/18. https://doi.org/10.1109/IC3e.2018.8632641

Shifting Dataset to Preserve Data Privacy. / Pozi, Muhammad Syafiq Mohd; Abu Bakar, Asmidar; Ismail, Roslan; Yussof, Salman; Abdul Rahim, Fiza; Ramli, Ramona.

2018 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2018. Institute of Electrical and Electronics Engineers Inc., 2019. p. 134-139 8632641 (2018 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2018).

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

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Pozi MSM, Abu Bakar A, Ismail R, Yussof S, Abdul Rahim F, Ramli R. Shifting Dataset to Preserve Data Privacy. In 2018 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 134-139. 8632641. (2018 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2018). https://doi.org/10.1109/IC3e.2018.8632641