Privacy preserving association rule mining by introducing concept of impact factor

Pathak, Kshitij; Chaudhari, Narendra S.; Tiwari, Aruna

doi:10.1109/iciea.2012.6360953

Cited by 16 publications

(7 citation statements)

References 8 publications

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“…This is to keep the computational complexity as low as possible. Chaudhari and Tiwari [30] work on heuristic-based association rule hiding using oracle real application clusters by introducing the concept of the impact factor of the transaction on the rule. Yadav and Ojha [31] work on data hiding in a generic grid that could be of pixels or bits.…”

Section: Output Privacymentioning

confidence: 99%

Enhanced Secured Map Reduce layer for Big Data privacy and security

2019

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Data security, according to the common definition is confidentiality, integrity, and availability of data. It is the act of guaranteeing that the information is safe from unauthorized access, ensures that the information is reliable and accurate which is accessible whenever it is required. An information security design incorporates features, for example, gathering the required data, protecting it, and obliterating any data that is never again required [1]. Privacy, on the other hand, is the appropriate use of information. In other words, merchants and companies should use the data provided to them only for the intended purpose. For example, if an individual purchase a product from XYZ Company and provides them with their personal information like address, card number, etc. then this company cannot sell that information to the third party. Companies need to enact a data security policy for the sole purpose of ensuring data privacy of their consumer personal information. Moreover, companies must ensure data privacy because the information is an asset to the company. However, no data security policy can overcome the

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Section: Output Privacymentioning

confidence: 99%

Enhanced Secured Map Reduce layer for Big Data privacy and security

2019

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“…Also included the merits and demerits of each technique involved in the association rule hiding process. Kshitij Pathak et al [2] Introduced a system for fast Privacy preserving association rule, including the idea of PC clusters for performing the task in parallel, in order to obtain improvised outcomes during execution time. Sunil Kumar et al [3] Suggested an algorithm for hiding association rules in data mining, thereby, reduced the number of modifications and hide more rules in less time.…”

Section: Background and Related Workmentioning

confidence: 99%

A Novel Approach of Association Rule Hiding Using DBCT (Distortion, Blocking and Cryptographic Technique)

Naidu¹,

Kumar²

2019

helix

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Associative rule hiding is a technique used in hiding sensitive data, during data processing to secure the sensitive association rules generated using association rule mining. Several methods were planned within the literature for hiding sensitive data items. Few apply distributed databases across various sites, few indulged data perturbation, and few utilized clustering and few of them employ data distortion technique. Algorithms supporting this method will follow either of the following two techniques. Hide a particular rule with the help of data alteration technique or hide the principles relying on the sensitivity of the items to be hidden. The proposed perspective dependent on data distortion technique which modifies the position of the sensitive items, yet its support is not at all altered and also used the ideology of representative results to shear the rules initially and then hides those sensitive rules. Experimental results exhibit that proposed method hides lot of rules at a minimum range of database scans in contrast to existing algorithms supporting data distortion technique.

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“…K. Pathak et al [19] proposed an approach that is based on concept of pc cluster (improve performance by running operations in parallel), impact factor (of a transaction is equal to number of itemsets that are present in those itemsets which represents sensitive association rule) and hybrid algorithm (which is a combination of ISL (Increase support of LHS) and DSR (Decrease support of RHS). This approach is able to reduce the execution time and maintain data quality.…”

Section: Recent Evolutionsmentioning

confidence: 99%

A Survey on Association Rule Hiding Methods

Jadav¹,

Vania²,

Patel³

2013

IJCA

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In recent years, the use of data mining techniques and related applications has increased a lot as it is used to extract important knowledge from large amount of data. This has increased the disclosure risks to sensitive information when the data is released to outside parties. Database containing sensitive knowledge must be protected against unauthorized access. Seeing this it has become necessary to hide sensitive knowledge in database. To address this problem, Privacy Preservation Data Mining (PPDM) include association rule hiding method to protect privacy of sensitive data against association rule mining. In this paper, we survey existing approaches to association rule hiding, along with some open challenges. We have also summarized few of the recent evolution. Keywords Association rule hidingAn association rule is in the form X => Y, where X and Y are the subsets of item set in I, XI, YI, and X∩Y=Ø. In the rule X => Y, where X is called the antecedent (left-hand-side) and Y is the consequent (right-hand-side). Association rule mining generates high number of rules and only few of them are of interest. To solve interest measurement problem, minimum support and minimum confidence thresholds are

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Privacy preserving association rule mining by introducing concept of impact factor

Cited by 16 publications

References 8 publications

Enhanced Secured Map Reduce layer for Big Data privacy and security

Enhanced Secured Map Reduce layer for Big Data privacy and security

A Novel Approach of Association Rule Hiding Using DBCT (Distortion, Blocking and Cryptographic Technique)

A Survey on Association Rule Hiding Methods

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