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

Electronics

Awais Ahmad¹  Zenab Amin²  Gwanggil Jeon³  Adeel Anjum⁴  Abid Khan⁵ 

[1] Department of Computer Science, Air University, Islamabad 44000, Pakistan;Department of Computer Science, COMSATS University Islamabad, Park Road, Chak Shahzad, Islamabad 44000, Pakistan;Department of Embedded Systems Engineering, Incheon National University, Incheon 22012, Korea;Institute of IT, Quaid-i-Azam University, Islamabad 45320, Pakistan;School of Computing and Engineering, University of Derby, Derby DE22 1GB, UK;

关键词: business intelligence;    privacy-preserving data publication;    high-dimensional data;    l-diversity;    constrained slicing;   

DOI  :  10.3390/electronics11081257

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown