Hiding Sensitive Association Rules Without Altering the Support of Sensitive Item(S)

Jain, Dhyanendra

doi:10.5121/ijaia.2012.3207

Cited by 17 publications

(18 citation statements)

References 12 publications

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“…Jain et al explore the data mining in privacy preservation in which the knowledge discovery process of finding the useful information and patterns out of large database [19]. In recent times data mining has gained immense importance as it paves way for the management to obtain hidden information and use them in decision-making for exposing it for the betterment of web mining.…”

Section: Literature Surveymentioning

confidence: 99%

A Hand to Hand Taxonomical Survey on Web Mining

Sharma¹,

Dubey²

2012

IJCA

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The data mining techniques exploitation in the field of web is referred as web mining. The enormous data is present at the websites and this need to be tackled well with the help of different data mining techniques. Searching, puling data together and analyzing the data are the main focus of web mining. The application of web mining is in the field of ecommerce and e-learning, web search, database, AI, information retrieval, system improvement etc. Information extraction from the web documents is a typical task and can be done efficiently after the through study of mining. This paper would facilitate to comprehend the concept of web mining by analyzing the facts retrieved from various sources. The paper presents the literature survey on web mining. It also explains the detailed view of three kinds of web mining techniques viz. web content mining, web structure mining and web usage mining. For the survey, different papers are analyzed and then presented as the study of web mining and its subtasks. KeywordsWeb mining,web content mining,web structure mining,web usage mining,information retrieval, information extraction.

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Section: Literature Surveymentioning

confidence: 99%

A Hand to Hand Taxonomical Survey on Web Mining

Sharma¹,

Dubey²

2012

IJCA

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“…In 2012, D. Jain et al [16], introduced an algorithm to hide sensitive association rules without changing the support of frequent itemsets. This algorithm used a new concept named Representative Rule RR, where all sensitive rules can be inferred without any access to the main database.…”

Section: Introductionmentioning

confidence: 99%

Privacy Preserving Association Rule Hiding Techniques: Current Research Challenges

Refaat¹,

Aboelseoud²,

Shafee³

et al. 2016

IJCA

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Association rule mining is one of the most used techniques of data mining that are utilized to extract the association rules from large databases. Association rules are one of the most significant assets of any organization that can be used for business growth and profitability increase. It contains sensitive information that threatens the privacy of its publication and it should be hidden before publishing the database. Privacy preserving data mining (PPDM) techniques is used to preserve such confidential information or restrictive patterns from unauthorized access. The pattern can be represented in the form of a frequent itemset or association rule. Also, a rule or pattern is marked as sensitive if its disclosure risk is above a given threshold. This paper discusses the current techniques and challenges of privacy preserving in association rule mining. Also, presentation of metrics used to evaluate the performance of those approaches is also given. Finally, Interesting future trends in this research body are specified.

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“…Readers looking for more information regarding these approaches can refer to [9]. At recent, many works have been focused on mining association rules at a single concept level [2], [7], [10], [11]. There are applications which need to find associations at multiple concept levels.…”

Section: Introductionmentioning

confidence: 99%

A Database Sanitizing Algorithm for Hiding Sensitive Multi-Level Association Rule Mining

Darwish¹,

Madbouly²,

El-Hakeem³

2014

IJCCE

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Abstract-The sharing of information has been proven to be beneficial for business partnerships in many application areas such as business planning or marketing. Today, association rule mining imposes threats to data sharing, since it may disclose patterns and various kinds of sensitive knowledge that are difficult to find. Such information must be protected against unauthorized access. The challenge is to protect actionable knowledge for strategic decisions, but at the same time not to lose the great benefit of association rule mining. To address this challenge, a sanitizing process transforms the source database into a released database in which the counterpart cannot extract sensitive rules from it. Unlike existing works that focused on hiding sensitive association rules at a single concept level, this paper emphasizes on building a sanitizing algorithm for hiding association rules at multiple concept levels. Employing multi-level association rule mining may lead to the discovery of more specific and concrete knowledge from datasets. The proposed system uses genetic algorithm as a biogeography-based optimization strategy for modifying multi-level items in database in order to minimize sanitization's side effects such as non-sensitive rules falsely hidden and fake rules falsely generated. The new approach is empirically tested and compared with other sanitizing algorithms depicting considerable improvement in completely hiding any given multi-level rule that in turn can fully support security of database and keeping the utility and certainty of mined multi-level rules at highest level.Index Terms-Database sanitization, genetic algorithm, privacy preserving data mining, multi-level association rule hiding.

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Hiding Sensitive Association Rules Without Altering the Support of Sensitive Item(S)

Cited by 17 publications

References 12 publications

A Hand to Hand Taxonomical Survey on Web Mining

A Hand to Hand Taxonomical Survey on Web Mining

Privacy Preserving Association Rule Hiding Techniques: Current Research Challenges

A Database Sanitizing Algorithm for Hiding Sensitive Multi-Level Association Rule Mining

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