Preserving Privacy of High-Dimensional Data by l-Diverse Constrained Slicing

Journal article


Amin, Z., Anjum, A., Khan, A., Ahmad, A. and Jeon, G. 2022. Preserving Privacy of High-Dimensional Data by l-Diverse Constrained Slicing. Electronics. 11 (8), p. 1257. https://doi.org/10.3390/electronics11081257
AuthorsAmin, Z., Anjum, A., Khan, A., Ahmad, A. and Jeon, G.
Abstract

In the modern world of digitalization, data growth, aggregation and sharing have escalated drastically. Users share huge amounts of data due to the widespread adoption of Internet-of-things (IoT) and cloud-based smart devices. Such data could have confidential attributes about various individuals. Therefore, privacy preservation has become an important concern. Many privacy-preserving data publication models have been proposed to ensure data sharing without privacy disclosures. However, publishing high-dimensional data with sufficient privacy is still a challenging task and very little focus has been given to propound optimal privacy solutions for high-dimensional data. In this paper, we propose a novel privacy-preserving model to anonymize high-dimensional data (prone to various privacy attacks including probabilistic, skewness, and gender-specific). Our proposed model is a combination of l-diversity along with constrained slicing and vertical division. The proposed model can protect the above-stated attacks with minimal information loss. The extensive experiments on real-world datasets advocate the outperformance of our proposed model among its counterparts.

Keywordsbusiness intelligence; privacy-preserving data publication; high-dimensional data; l-diversity; constrained slicing
Year2022
JournalElectronics
Journal citation11 (8), p. 1257
PublisherMDPI
ISSN2079-9292
Digital Object Identifier (DOI)https://doi.org/10.3390/electronics11081257
Web address (URL)https://www.mdpi.com/2079-9292/11/8/1257
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Output statusPublished
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Online15 Apr 2022
Publication process dates
Accepted02 Apr 2022
Deposited16 Dec 2022
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