Employing PRBAC for privacy preserving data publishing

Dafa-Alla, Anour F.A.; Sohn, Gyoyong; Ryu, Keun Ho

doi:10.1145/1655925.1656186

Cited by 1 publication

(2 citation statements)

References 14 publications

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“…Dafa-Alla et al [18] employed PRBAC in order to protect sensitive information while different users are working in sessions according to their roles with variable privileges. Such control of sensitivity is dependent upon relational format.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This technique has five components such as rule miner, result recommender, query miner, query re-commender and querier [29]. The ultimate goal of this technique is to provide the best query for the user amongst the frequently asked ones but remains silent about security risks involvement as suggested by Dafa-Alla et al [18]. Furthermore, Wang et al [30] suggested SDST (Standard Data Source Template) for mining XML file.…”

Section: Literature Reviewmentioning

confidence: 99%

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Hiding Sensitive XML Association Rules With Supervised Learning Technique

Iqbal¹,

Asghar²,

Mirza³

2011

IIM

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In the privacy preservation of association rules, sensitivity analysis should be reported after the quantification of items in terms of their occurrence. The traditional methodologies, used for preserving confidentiality of association rules, are based on the assumptions while safeguarding susceptible information rather than recognition of insightful items. Therefore, it is time to go one step ahead in order to remove such assumptions in the protection of responsive information especially in XML association rule mining. Thus, we focus on this central and highly researched area in terms of generating XML association rule mining without arguing on the disclosure risks involvement in such mining process. Hence, we described the identification of susceptible items in order to hide the confidential information through a supervised learning technique. These susceptible items show the high dependency on other items that are measured in terms of statistical significance with Bayesian Network. Thus, we proposed two methodologies based on items probabilistic occurrence and mode of items. Additionally, all this information is modeled and named PPDM (Privacy Preservation in Data Mining) model for XARs. Furthermore, the PPDM model is helpful for sharing markets information among competitors with a lower chance of generating monopoly. Finally, PPDM model introduces great accuracy in computing sensitivity of items and opens new dimensions to the academia for the standardization of such NP-hard problems

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Section: Literature Reviewmentioning

confidence: 99%