Classification and Evaluation of Privacy Preserving Data Mining Methods

Nasiri, Negar; Keyvanpour, Mohammad Reza

doi:10.52547/ijict.12.3.27

Cited by 2 publications

(2 citation statements)

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“…Most of these studies (Dutta and Guppta 2016;Sharma and Ahuja 2019;Dhanalakshmi and Siva Sankari 2014;Md Siraj et al 2019) summarize and evaluate the existing PPDM techniques while other studies talk about challenges and possible improvements for enhancing PPDM methods (Patel and Kotecha 2017;Abdul et al 2015;Vishwakarma et al 2016;Malik et al 2012). Studies such as Kiran and Vasumathi (2018), Nasiri and Keyvanpour (2020), Dutta and Guppta (2016) present frameworks and categorizations of existing PPDM methods to provide the overall picture of the PPDM methods.…”

Section: Problem Identificationmentioning

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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Section: Problem Identificationmentioning

confidence: 99%

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

2023

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“…According to what is stated in [59][60][61][62][63][64], a new algorithm for quickly hiding sensitive association rules has been developed. Within this algorithm, a heuristic function is used to further determine the earlier weight for each specific transaction.…”

Section: Related Workmentioning

confidence: 99%

Privacy Preserving Data Mining Framework for Negative Association Rules: An Application to Healthcare Informatics

Darwish

Essa²,

Osman³

et al. 2022

IEEE Access

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Protecting the privacy of healthcare information is an important part of encouraging data custodians to give accurate records so that mining may proceed with confidence. The application of association rule mining in healthcare data has been widespread to this point in time. Most applications focus on positive association rules, ignoring the negative consequences of particular diagnostic techniques. When it comes to bridging divergent diseases and drugs, negative association rules may give more helpful information than positive ones. This is especially true when it comes to physicians and social organizations (e.g., a certain symptom will not arise when certain symptoms exist). Data mining in healthcare must be done in a way that protects the identity of patients, especially when dealing with sensitive information. However, revealing this information puts it at risk of attack. Healthcare data privacy protection has lately been addressed by technologies that disrupt data (data sanitization) and reconstruct aggregate distributions in the interest of doing research in data mining. In this study, metaheuristic-based data sanitization for healthcare data mining is investigated in order to keep patient privacy protected. It is hoped that by using the Tabu-genetic algorithm as an optimization tool, the suggested technique chooses item sets to be sanitized (modified) from transactions that satisfy sensitive negative criteria with the goal of minimizing changes to the original database. Experiments with benchmark healthcare datasets show that the suggested privacy preserving data mining (PPDM) method outperforms existing algorithms in terms of Hiding Failure (HF), Artificial Rule Generation (AR), and Lost Rules (LR).

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Classification and Evaluation of Privacy Preserving Data Mining Methods

Cited by 2 publications

References 1 publication

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

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

Privacy Preserving Data Mining Framework for Negative Association Rules: An Application to Healthcare Informatics

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