Random Response Forest for Privacy-Preserving Classification
Author
Source
Journal of Computational Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-11-14
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
The paper deals with classification in privacy-preserving data mining.
An algorithm, the Random Response Forest, is introduced constructing many binary decision trees, as an extension of Random Forest for privacy-preserving problems.
Random Response Forest uses the Random Response idea among the anonymization methods, which instead of generalization keeps the original data, but mixes them.
An anonymity metric is defined for undistinguishability of two mixed sets of data.
This metric, the binary anonymity, is investigated and taken into consideration for optimal coding of the binary variables.
The accuracy of Random Response Forest is presented at the end of the paper.
American Psychological Association (APA)
Szűcs, Gábor. 2013. Random Response Forest for Privacy-Preserving Classification. Journal of Computational Engineering،Vol. 2013, no. 2013, pp.1-6.
https://search.emarefa.net/detail/BIM-468905
Modern Language Association (MLA)
Szűcs, Gábor. Random Response Forest for Privacy-Preserving Classification. Journal of Computational Engineering No. 2013 (2013), pp.1-6.
https://search.emarefa.net/detail/BIM-468905
American Medical Association (AMA)
Szűcs, Gábor. Random Response Forest for Privacy-Preserving Classification. Journal of Computational Engineering. 2013. Vol. 2013, no. 2013, pp.1-6.
https://search.emarefa.net/detail/BIM-468905
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-468905