Classification and Evaluation of Privacy Preserving Data Mining Methods

Nasiri, Negar; Keyvanpour, Mohammad Reza

doi:10.1109/ikt51791.2020.9345620

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…Public Key Encryption, Secret sharing, Homomorphic Encryption, Secure Multi-party computation (MPC), etc. are cryptography based techniques for preserving privacy in distributed data mining [14].…”

Section: Cryptography Based Approachmentioning

confidence: 99%

“…Secret sharing method is a set of two functions sharing function and relation function. Sharing function takes secret as an input and divides secret into n secret shares and relation function reproduce (recovery of secret from n-1 shares is impossible) secret from n shares of secret [14].…”

Section: Cryptography Based Approachmentioning

confidence: 99%

“…2.3Data perturbation based approach:Data perturbation replace original values with some artificial data values so that statisticallycomputed information from perturbed data is not significantly different from statistically computed information from original data. Perturbation of data is performed with the help of data swapping, additive noise, and data Masking [14].Data perturbation approach classified into probability distribution and value distortion [18]. In probability distribution approachdata is replaced with the distribution itself or with other sample taken from same distribution, and in value distortion approach data elements are perturbed by randomization procedures (either multiplicative noise, additive noise, or some other).…”

Section: Cryptography Based Approachmentioning

confidence: 99%

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A Study on Privacy Preserving Big Data Mining: Techniques and Challenges

Dahiya¹

2022

ijarcs

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The basic goal of data mining algorithms is to extract previously undiscovered patterns from the data. When mining the data, sensitive and confidential information should be secured simultaneously to protect privacy. Due to the widespread use of information technology, enormous amounts of data are being produced at an exponential rate by several organisations, including hospitals, insurance providers, banks,e-commerce, and stock exchanges, making privacy a crucial concern in data mining. Anonymization, Perturbation, Generalization, and Cryptography are some of the privacy-preserving data mining techniques that have been proposed in the literature. In this study, we have reviewed all of these state of art techniques and presented a tabular comparison of work done by different authors as well as discussed the challenges of privacy preserving data mining.

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Section: Cryptography Based Approachmentioning

confidence: 99%

Section: Cryptography Based Approachmentioning

confidence: 99%

Section: Cryptography Based Approachmentioning

confidence: 99%

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A Study on Privacy Preserving Big Data Mining: Techniques and Challenges

Dahiya¹

2022

ijarcs

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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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