A Survey on Association Rule Hiding Methods

Jadav, Khyati B.; Vania, Jignesh; Patel, Dhiren

doi:10.5120/14177-2357

Cited by 6 publications

(1 citation statement)

References 26 publications

(27 reference statements)

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“…After that on the basis of disclosure threshold supplied by users, it hides the restricted patterns by sanitizing the percentage of the selected transactions. In [10] authors summarize the advantages and limitat ions of associations hiding approaches. C N Modi et al [5] proposed a heuristic algorith m named DSRRC (Decrease Support of R.H.S.…”

Section: E Cryptography Based Approachesmentioning

confidence: 99%

A Survey on Association Rule Hiding Approaches

2015

IJAERD

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Abstract-In recent years, explosive growth of the amount of data gathered by transactional systems, a challenge for finding new techniques to extract useful patterns from such a huge amount of data arose. As the database is growing day by day the organizations which maintain this database are worried about the importance of such huge transaction database. One of the greatest challenging tasks of data mining is finding hidden patterns without revealing sensitive information. Privacy preserving data mining (PPDM) is the recent research area that deals with the problem of hiding the sensitive information while analyzing data. PPDM algorithms are evolved for modifying the original data in such that the no sensitive information is revealed even after mining procedure. Association rule hiding is one of the privacy preservation techniques to hide sensitive association rules. All association rule hiding algorithms focus to minimally modify the original database such that no sensitive association rule is derived from it. This paper contains the comprehensive survey of privacy preserving data mining methods. Advantages and disadvantages of the existing algorithms are discussed in brief.

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Section: E Cryptography Based Approachesmentioning

confidence: 99%

A Survey on Association Rule Hiding Approaches

2015

IJAERD

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Determination of Avalanche Effect to Compute the Efficiency of Association Rule Hiding Algorithms

Audichya¹,

Sharma²,

Vaishnav³

2022

Artificial Intelligence and Sustainable Computing

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Ak-Anonymous Rule Clustering Approach for Data Publishing

Ohki¹,

Inuiguchi²

2017

JACIII

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Classification rules should be open for public inspection to ensure fairness.These rules can be originally induced from some dataset. If induced classification rules are supported only by a small number of objects in the dataset, publication can lead to identification of objects supporting the rule, given their speciality. Eventually, it is possible to retrieve information about the identified objects. This identifiability is not desirable in terms of data privacy.In this paper, to avoid such privacy breaches, we propose rule clustering for achievingk-anonymity of all induced rules, i.e., the induced rules are supported by at leastkobjects in the dataset. The proposed approach merges similar rules to satisfyk-anonymity while aiming to maintain the classification accuracy. Two numerical experiments were executed to verify both the accuracy of the classifier with the rules obtained by the proposed method and the ratio of decision classes revealed from leaked information about objects. The experimental results show the usefulness of the proposed method.

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A Survey on Association Rule Hiding Methods

Cited by 6 publications

References 26 publications

A Survey on Association Rule Hiding Approaches

A Survey on Association Rule Hiding Approaches

Determination of Avalanche Effect to Compute the Efficiency of Association Rule Hiding Algorithms

Ak-Anonymous Rule Clustering Approach for Data Publishing

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