A Review on Security Techniques for Privacy-Preserving in Data Mining

As the rise in the data mining algorithm the extraction of knowledge from the large data is getting easy. But due to this new problem of Privacy of the knowledge from the stored data at various servers is introduced. So, it is required to provide privacy of the sensitive data from the data miners. This paper focuses on various approaches implemented by the miners for preserving of information at individual level, class level, etc. A detail description with limitation of different techniques security of privacy preserving is explained.

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A NOVEL PRIVACY PRESERVING DATA MINING ALGORITHM

International Conference on Aerospace Sciences and Aviation Technology ◽

10.21608/asat.2011.27210 ◽

2011 ◽

Vol 11 (ASAT CONFERENCE) ◽

pp. 1-17

Author(s):

Fahmy Aly ◽

Fakhry Medhat ◽

M. Hanafy ◽

EI-Zeweidy Aly

Keyword(s):

Data Mining ◽

Privacy Preserving ◽

Data Mining Algorithm ◽

Privacy Preserving Data Mining ◽

Mining Algorithm

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A New Privacy Preserving Data Mining Algorithm using Data Distortion Techniques

Journal of Computer and Mathematical Sciences ◽

10.29055/jcms/778 ◽

2018 ◽

Vol 9 (5) ◽

pp. 404-414

Author(s):

Mohana Chelvan P ◽

Perumal K

Keyword(s):

Data Mining ◽

Privacy Preserving ◽

Data Mining Algorithm ◽

Privacy Preserving Data Mining ◽

Data Distortion ◽

Mining Algorithm ◽

Using Data

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A Kind of Privacy-Preserving Data Mining Algorithm Oriented to Data User

Advances in Intelligent and Soft Computing - Advances in Multimedia, Software Engineering and Computing Vol.2 ◽

10.1007/978-3-642-25986-9_4 ◽

2011 ◽

pp. 25-30 ◽

Cited By ~ 1

Author(s):

Li Cai ◽

JianYing Su

Keyword(s):

Data Mining ◽

Privacy Preserving ◽

Data Mining Algorithm ◽

Privacy Preserving Data Mining ◽

Mining Algorithm ◽

Data User

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Secure Data Analysis in Clusters (Iris Database)

Advances in Business Information Systems and Analytics - Handbook of Research on Advanced Data Mining Techniques and Applications for Business Intelligence ◽

10.4018/978-1-5225-2031-3.ch004 ◽

2017 ◽

pp. 52-61

Author(s):

Raghvendra Kumar ◽

Prasant Kumar Pattnaik ◽

Priyanka Pandey

Keyword(s):

Data Mining ◽

Data Analysis ◽

Privacy Preservation ◽

Data Communication ◽

Distributed Database ◽

Privacy Preserving ◽

Data Mining Algorithm ◽

Data Set ◽

Mining Algorithm ◽

Secure Data

This chapter used privacy preservation techniques (Data Modification) to ensure Privacy. Privacy preservation is another important issue. A picture, where number of clients owning their clustered databases (Iris Database) wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information and requires the privacy of the privileged information. There are numbers of efficient protocols are required for privacy preserving in data mining. This chapter presented various privacy preserving protocols that are used for security in clustered databases. The Xln(X) protocol and the secure sum protocol are used in mutual computing, which can defend privacy efficiently. Its focuses on the data modification techniques, where it has been modified our distributed database and after that sanded that modified data set to the client admin for secure data communication with zero percentage of data leakage and also reduce the communication and computation complexity.

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Stable Feature Selection with Privacy Preserving Data Mining Algorithm

Communications in Computer and Information Science - Advanced Informatics for Computing Research ◽

10.1007/978-981-10-5780-9_21 ◽

2017 ◽

pp. 227-237 ◽

Cited By ~ 1

Author(s):

Mohana Chelvan P ◽

Perumal K

Keyword(s):

Data Mining ◽

Feature Selection ◽

Privacy Preserving ◽

Data Mining Algorithm ◽

Privacy Preserving Data Mining ◽

Mining Algorithm ◽

Stable Feature

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PPDM-TAN: A Privacy-Preserving Multi-Party Classifier

Computation ◽

10.3390/computation9010006 ◽

2021 ◽

Vol 9 (1) ◽

pp. 6

Author(s):

Maria Eleni Skarkala ◽

Manolis Maragoudakis ◽

Stefanos Gritzalis ◽

Lilian Mitrou

Keyword(s):

Data Mining ◽

Private Information ◽

Security Analysis ◽

Privacy Preserving ◽

Data Mining Algorithm ◽

Bayes Classifier ◽

Sensitive Data ◽

Privacy Concerns ◽

Information Privacy Concerns ◽

Private Data

Distributed medical, financial, or social databases are analyzed daily for the discovery of patterns and useful information. Privacy concerns have emerged as some database segments contain sensitive data. Data mining techniques are used to parse, process, and manage enormous amounts of data while ensuring the preservation of private information. Cryptography, as shown by previous research, is the most accurate approach to acquiring knowledge while maintaining privacy. In this paper, we present an extension of a privacy-preserving data mining algorithm, thoroughly designed and developed for both horizontally and vertically partitioned databases, which contain either nominal or numeric attribute values. The proposed algorithm exploits the multi-candidate election schema to construct a privacy-preserving tree-augmented naive Bayesian classifier, a more robust variation of the classical naive Bayes classifier. The exploitation of the Paillier cryptosystem and the distinctive homomorphic primitive shows in the security analysis that privacy is ensured and the proposed algorithm provides strong defences against common attacks. Experiments deriving the benefits of real world databases demonstrate the preservation of private data while mining processes occur and the efficient handling of both database partition types.

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