Transformation approach for boolean attributes in privacy preserving data mining

Kaur, Rupinder; Bansal, Meenakshi

doi:10.1109/ngct.2015.7375200

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Some research work have proposed interesting PPDM techniques but have not paid much attention to the accuracy-privacy trade-off (Tsai et al 2016;Tang et al 2019;Ashok and Mukkamala 2011;Hong et al 2010;Meghanathan et al 2014;Li and Wang 2011;Kaur and Bansal 2016;Gokulnath et al 2015;Oishi 2017;Lin et al 2015;Miyaji and Rahman 2011;Ketel and Homaifar 2005;Hong et al 2011).…”

Section: Accuracy-privacy Trade-off In Generic Ppdm Methodsmentioning

confidence: 99%

Privacy-preserving data (stream) mining techniques and their impact on data mining accuracy: a systematic literature review

2023

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This study investigates existing input privacy-preserving data mining (PPDM) methods and privacy-preserving data stream mining methods (PPDSM), including their strengths and weaknesses. A further analysis was carried out to determine to what extent existing PPDM/PPDSM methods address the trade-off between data mining accuracy and data privacy which is a significant concern in the area. The systematic literature review was conducted using data extracted from 104 primary studies from 5 reputed databases. The scope of the study was defined using three research questions and adequate inclusion and exclusion criteria. According to the results of our study, we divided existing PPDM methods into four categories: perturbation, non-perturbation, secure multi-party computation, and combinations of PPDM methods. These methods have different strengths and weaknesses concerning the accuracy, privacy, time consumption, and more. Data stream mining must face additional challenges such as high volume, high speed, and computational complexity. The techniques proposed for PPDSM are less in number than the PPDM. We categorized PPDSM techniques into three categories (perturbation, non-perturbation, and other). Most PPDM methods can be applied to classification, followed by clustering and association rule mining. It was observed that numerous studies have identified and discussed the accuracy-privacy trade-off. However, there is a lack of studies providing solutions to the issue, especially in PPDSM.

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