Preserving Privacy of High-Dimensional Data by l-Diverse Constrained Slicing
Journal article
Authors | Amin, Z., Anjum, A., Khan, A., Ahmad, A. and Jeon, G. |
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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. |
Keywords | business intelligence; privacy-preserving data publication; high-dimensional data; l-diversity; constrained slicing |
Year | 2022 |
Journal | Electronics |
Journal citation | 11 (8), p. 1257 |
Publisher | MDPI |
ISSN | 2079-9292 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/electronics11081257 |
Web address (URL) | https://www.mdpi.com/2079-9292/11/8/1257 |
Accepted author manuscript | File Access Level Open |
Publisher's version | License File Access Level Open |
Output status | Published |
Publication dates | |
Online | 15 Apr 2022 |
Publication process dates | |
Accepted | 02 Apr 2022 |
Deposited | 16 Dec 2022 |
https://repository.derby.ac.uk/item/9vy96/preserving-privacy-of-high-dimensional-data-by-l-diverse-constrained-slicing
Download files
Accepted author manuscript
electronics-11-01257-v2.pdf | ||
File access level: Open |
Publisher's version
electronics-11-01257-v2.pdf | ||
License: CC BY 4.0 | ||
File access level: Open |
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