Survey on Privacy Preserving Data Mining Techniques using Recent Algorithms

Rajesh, N.; Sujatha, K.; Selvakumar, A. Arul Lawrence

doi:10.5120/ijca2016907917

Cited by 11 publications

(10 citation statements)

References 35 publications

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“…In the literature so far, to the best of our knowledge, insufficient attention has been paid to the systematization of techniques and methods related to PPDM. Some of the authors have systematized previous research in this area (see Rajesh, Sujatha, & Arul, 2016;Shah & Gulati, 2016;Vaghashia & Ganatra, 2015;.…”

Section: Techniques and Methodsmentioning

confidence: 99%

Data mining privacy preserving: Research agenda

Kreso

Kapo

Turulja³

2020

WIREs Data Min & Knowl

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In the modern days, the amount of the data and information is increasing along with their accessibility and availability, due to the Internet and social media. To be able to search this vast data set and to discover unknown useful data patterns and predictions, the data mining method is used. Data mining allows for unrelated data to be connected in a meaningful way, to analyze the data, and to represent the results in the form of useful data patterns and predictions that help and predict future behavior. The process of data mining can potentially violate sensitive and personal data. Individual privacy is under attack if some of the information leaks and reveals the identity of a person whose personal data were used in the data mining process. There are many privacy‐preserving data mining (PPDM) techniques and methods that have a task to preserve the privacy and sensitive data while providing accurate data mining results at the same time. PPDM techniques and methods incorporate different approaches that protect data in the process of data mining. The methodology that was used in this article is the systematic literature review and bibliometric analysis. This article identifieds the current trends, techniques, and methods that are being used in the privacy‐preserving data mining field to make a clear and concise classification of the PPDM methods and techniques with possibly identifying new methods and techniques that were not included in the previous classification, and to emphasize the future research directions. This article is categorized under: Commercial, Legal, and Ethical Issues > Security and Privacy

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Section: Techniques and Methodsmentioning

confidence: 99%

Data mining privacy preserving: Research agenda

Kreso

Kapo

Turulja³

2020

WIREs Data Min & Knowl

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“…Three types of methods for partitioning data to be securely shared are well known. [3,4,23,24] They are horizontal, vertical and arbitrary partitioning methods. In the following, the horizontal method is only explained by using a data example of students' marks shown in Table 1.…”

Section: Introductionmentioning

confidence: 99%

“…The spreading of cloud computing enables big data analysis, which analyzes enormous information accumulated by the client and creates a market value of data. [1][2][3][4] On the other hand, the client of cloud computing is concerned about abuse or leak of information. For this purpose, privacy preserving data processing can be achieved in various ways by use of randomization techniques, cryptographic algorithms, anonymization methods, etc.…”

Section: Introductionmentioning

confidence: 99%

A proposal of privacy preserving reinforcement learning for secure multiparty computation

Miyajima

Shigei

Makino

et al. 2017

AIR

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Many studies have been done with the security of cloud computing. Though data encryption is a typical approach, high computing complexity for encryption and decryption of data is needed. Therefore, safe system for distributed processing with secure data attracts attention, and a lot of studies have been done. Secure multiparty computation (SMC) is one of these methods. Specifically, two learning methods for machine learning (ML) with SMC are known. One is to divide learning data into several subsets and perform learning. The other is to divide each item of learning data and perform learning. So far, most of works for ML with SMC are ones with supervised and unsupervised learning such as BP and K-means methods. It seems that there does not exist any studies for reinforcement learning (RL) with SMC. This paper proposes learning methods with SMC for Q-learning which is one of typical methods for RL. The effectiveness of proposed methods is shown by numerical simulation for the maze problem.

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“…[4,5] Privacy preserving data mining can be achieved in various ways by use of randomization techniques, cryptographic algorithms, anonymization methods, etc. [4,[6][7][8][9] Specifically, data encryption seems to be effective. However, data encryption system requires both encryption and decryption for requests of client or user, so its applications are limited.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, studies with distributed processing for secure data have been attracting attention. [6][7][8][9] As one of these studies, secure multiparty computation (SMC) has been introduced. [10][11][12] The purpose of SMC is to allow parties to carry out distributed computing tasks in secure way.…”

Section: Introductionmentioning

confidence: 99%

New privacy preserving clustering methods for secure multiparty computation

Miyajima

Shigei

Miyajima

et al. 2016

AIR

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Many researches on privacy preserving data mining have been done. Privacy preserving data mining can be achieved in various ways by use of randomization techniques, cryptographic algorithms, anonymization methods, etc. Further, in order to increase the security of data mining, secure multiparty computation (SMC) has been introduced. Most of works in SMC are developed on applying the model of SMC on different data distributions such as vertically, horizontally and arbitrarily partitioned data. Another type of SMC with sharing data itself to each party attracts attention, and some studies have been done. A simple method to share data was proposed and it was applied to statistical computation. However, for SMC, complicated computation such as data mining has never been proposed. In the previous paper, we proposed a BP learning for SMC and showed the effectiveness of it. In this paper, we propose clustering methods such as k-means and NG for SMC and show the effectiveness in numerical simulation.

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Survey on Privacy Preserving Data Mining Techniques using Recent Algorithms

Cited by 11 publications

References 35 publications

Data mining privacy preserving: Research agenda

Data mining privacy preserving: Research agenda

A proposal of privacy preserving reinforcement learning for secure multiparty computation

New privacy preserving clustering methods for secure multiparty computation

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