A Survey on Privacy Preserving Data Mining

Wang, Jian; Luo, Yu; Zhao, Yan; Le, Jiajin

doi:10.1109/dbta.2009.147

Cited by 59 publications

(25 citation statements)

References 16 publications

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“…The noise added is rightfully large so that the individual values of the records can no longer be recovered by any adversary. Also explain by Pingshui Wang in [16] In general, randomization method aims at finding an appropriate balance between privacy preservation and knowledge discovery. Representative randomization methods include random-noise based perturbation and Randomized Response scheme.…”

Section: Randomizationmentioning

confidence: 99%

A Review of Privacy Preservation Technique

Singh¹,

Keer²,

Motwani³

2014

IJCA

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Privacy-preserving is one of the most important challenges in a computer world, because of the huge amount of sensitive information on the internet. The paper contains several privacy preservation techniques for data publishing in the real world. There are several privacy attacks are associate but among of them mainly two attacks are record linkage and attribute linkage. Many scientists have proposed methods to preserve the privacy of data publishing such as K-anonymity, ℓ-diversity, t-closeness. K-anonymity can prevent the record linkage but unable to protect attribute linkage. ℓ-diversity technique overcomes the drawback of k-anonymity technique but it fail to protect from membership discloser attack. Tcloseness technique prevents to attribute discloser attack but it fail in identity disclosure attack. Its computational complexity is large. In this paper we present the novel technique call slicing which to be implemented with various data set through prevent the privacy preservation for data publishing. The goals of this paper is re-analysis a number of privacy preservation of data mining technique clearly and then study the advantages and disadvantages of this technique.

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Section: Randomizationmentioning

confidence: 99%

A Review of Privacy Preservation Technique

Singh¹,

Keer²,

Motwani³

2014

IJCA

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“…The basic review is done to get into the knowledge of privacy preserving data mining [3,4,5]. As a part of this study various techniques have been identified and studied that could be used to preserve the user"s data sensitivity.…”

Section: Introductionmentioning

confidence: 99%

A Study of Correlation Impact on Privacy Preserving Data Mining

Hyma¹,

Reddy²,

Damodaram³

2015

IJCA

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Data sharing is obvious in present day scenario of digital world, and when data is being shared among various application areas the sensitive data of the individuals is disclosed to the public. An evident awareness about this privacy violation has been created among the people now when compared to the earlier days and they are also showing a real concern towards their privacy in the technology enabled digital world. At one end several studies have been proved that privacy is a primary concern and also suggesting not to disclose too much of individual information, but at the other end people are disclosing their personal information knowingly or unknowingly through online surveys, social networks, online shopping sites, e-commerce, government agencies etc. This information sharing is obvious and it can"t be unavoidable. Consequently several techniques have been proposed to protect privacy of the individual disclosed information, but still there is an immense need of new privacy preserving techniques that can equally accommodate with the proportionate expansion of the digital data. Existing privacy techniques applied on the data set assuming all the records are independently sampled, where as in the real world data set the correlations among the records is obvious and needs to be studied to achieve accurate privacy protection. This paper provides an overview of the development of privacy preserving models and the further enhancements to be carried out to accommodate with the diverse privacy requirements and data utilization along with the correlation study.

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“…Wang Wang (2010), adds stating that the main objective of PPDM is to develop data mining methods without increasing the risk of mishandling the data used. These techniques use some form of modification to the original data to accomplish the privacy preservation.…”

Section: Privacy Preserving Data Miningmentioning

confidence: 99%