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

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.

Improved angelization technique against background knowledge attack for 1:M microdata

“…Modern technologies, such as the Internet of Things (IoT) and Big Data are the key enablers to revolutionizing today’s modern society in different fields, for example, the Electronic Health Records (EHRs) ( Dang et al., 2019 ; Muftuoglu, Kızrak & Yildirim, 2022 ; Amin et al., 2022 ). The government or private organizations collect the EHRs via the IoT devices and share them for further statistical analysis and policymaking ( Moonsamy & Singh, 2022 ; Sheikhtaheri et al., 2022 ).…”