Privacy preserving sub-feature selection based on fuzzy probabilities

Bhuyan, Hemanta Kumar; Kamila, Narendra Kumar

doi:10.1007/s10586-014-0393-9

Cited by 23 publications

(6 citation statements)

References 29 publications

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“…The authors of [22] developed a clustering-based anonymization approach to preserve the characteristics of streaming data. [23] framed a privacy protective sub-feature choice approach mostly based on fuzzy possibilities. Recently, in [24] the authors found that k-anonymity will be achieved by non-homogeneous generalization, and proposed a method named ring generalization to realize higher utility whereas providing identical privacy guarantee.…”

Section: Related Workmentioning

confidence: 99%

Utility Enhancement of Deficient Relational Recordset Anonymization

Samraj¹,

Appusamy²,

Shankar³

2018

IJIES

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Increase in the use of digital platforms generate a very huge amount of user data which on processing reveals more valuable information about users while mining or it may also reveal some future events. Privacy preserving data mining (PPDM) is the current developing area of research that precisely ensures a certain level of privacy by increasing the utility of information. Data Anonymization is a PPDM technique that protects sensitive information in the recordset with high confidence. Anonymizing recordsets that consists of null values habitually referred as deficient recordsets, suffer from serious information loss owing to null value contamination. This would be arisen because of the null values present in the original recordset. In this paper, we proposed an enhanced ARX framework to anonymize the deficient recordsets through a higher level of privacy and utility. We explore the characteristics of null value contamination on generalization, driven by these characteristics, we formulate an Enhanced ARX Anonymization-(,)Anonymity Model to preserve data utility on deficient recordsets. The results obtained by executing our framework over the real-world dataset proved to be optimized in providing better tradeoff between utility and privacy for deficient recordsets in Cell oriented Anonymization (CoA), Attribute oriented Anonymization (AoA) and Record oriented Anonymization (RoA) than the existing procedures.

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Section: Related Workmentioning

confidence: 99%

Utility Enhancement of Deficient Relational Recordset Anonymization

Samraj¹,

Appusamy²,

Shankar³

2018

IJIES

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“…Since few papers on sub-feature selection are available in different sites, it only considers the concept of sub-features that explained in [10,11] and helped to design the model of sub-feature selection for the proposed work. Next, it considers the framework for correlation among features for classification.…”

Section: B Approaches Of Sub-feature Selectionmentioning

confidence: 99%

“…Further the new feature can be included for analyzing the classification problem, but the concept of tiny feature data from each feature is considered for generating new class. Initially, Bhuyan and Kamila have initiated to design the concept of sub-feature data and applied in [10,11] using different database. Although they have used the sub-feature selection data as their own model, still it needs to develop different sub-feature selection model for classification.…”

Section: Introductionmentioning

confidence: 99%

Selection of Features on Mining Techniques for Classification

Kumar¹,

Mohan²,

Prabakaran³

2018

AJCST

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Feature selection has been developed by several mining techniques for classification. Some existing approaches couldn’t remove the irrelevant data from dataset for class. Thus it needs the selection of appropriate features that emphasize its role in classification. For this it consider the statistical method like correlation coefficient to identify the features from feature set whose data are very important for existing classes. The several methods such as Gaussian process, linear regression and Euclidean distance have taken into consideration for clarity of classification. The experimental results reveal that the proposed method identifies the exact relevant features for several classes.

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“…Some other references [20][21][22][23][24] also discussed privacy preserving methods based on fuzzy theory from different aspects. Reference [20] addressed the problem of privacy preserving in data mining by transforming the sensitive attributes to fuzzy attributes (for example: very low, low, medium, high, very high).…”

Section: Related Workmentioning

confidence: 99%