Shifting Dataset to Preserve Data Privacy
Classification (of information); Data mining; E-learning; Large dataset; Learning systems; Classification tasks; Data attributes; Dataset shifts; Generative model; Kernel Density Estimation; Privacy preservation; Privacy preserving; Synthetic data; Data privacy
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2023
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1833409953254080512 |
|---|---|
| author | Pozi M.S.M. Bakar A.A. Ismail R. Yussof S. Rahim F.A. Ramli R. |
| author2 | 57219746822 |
| author_facet | 57219746822 Pozi M.S.M. Bakar A.A. Ismail R. Yussof S. Rahim F.A. Ramli R. |
| author_sort | Pozi M.S.M. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Classification (of information); Data mining; E-learning; Large dataset; Learning systems; Classification tasks; Data attributes; Dataset shifts; Generative model; Kernel Density Estimation; Privacy preservation; Privacy preserving; Synthetic data; Data privacy |
| format | Conference Paper |
| id | my.uniten.dspace-24806 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | my.uniten.dspace-248062023-05-29T15:27:20Z Shifting Dataset to Preserve Data Privacy Pozi M.S.M. Bakar A.A. Ismail R. Yussof S. Rahim F.A. Ramli R. 57219746822 35178991300 15839357700 16023225600 57350579500 57191413657 Classification (of information); Data mining; E-learning; Large dataset; Learning systems; Classification tasks; Data attributes; Dataset shifts; Generative model; Kernel Density Estimation; Privacy preservation; Privacy preserving; Synthetic data; Data privacy 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. � 2018 IEEE. Final 2023-05-29T07:27:20Z 2023-05-29T07:27:20Z 2019 Conference Paper 10.1109/IC3e.2018.8632641 2-s2.0-85062865666 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062865666&doi=10.1109%2fIC3e.2018.8632641&partnerID=40&md5=897b2e7589c1cd52655fd36105fe3a96 https://irepository.uniten.edu.my/handle/123456789/24806 8632641 134 139 Institute of Electrical and Electronics Engineers Inc. Scopus |
| spellingShingle | Pozi M.S.M. Bakar A.A. Ismail R. Yussof S. Rahim F.A. Ramli R. Shifting Dataset to Preserve Data Privacy |
| title | Shifting Dataset to Preserve Data Privacy |
| title_full | Shifting Dataset to Preserve Data Privacy |
| title_fullStr | Shifting Dataset to Preserve Data Privacy |
| title_full_unstemmed | Shifting Dataset to Preserve Data Privacy |
| title_short | Shifting Dataset to Preserve Data Privacy |
| title_sort | shifting dataset to preserve data privacy |
| url_provider | http://dspace.uniten.edu.my/ |