Abstract
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.
Highlights
The combination of statistics, artificial intelligence, and database makes data mining and knowledge discovery a hot research area
The reasons behind the privacy preserving data mining are the existence of typical problems in data mining and knowledge discovery including clustering, classification, sequential patterning and association rule mining [8]
The question arises, “In how many rules, did we achieve success in preserving the privacy of XML association rules?” To answer this question, we have presented the total number of sensitive rules and the number of attributes considered by the proposed technique as well as the existing techniques [12,19]
Summary
The combination of statistics, artificial intelligence, and database makes data mining and knowledge discovery a hot research area The purpose of such development is to find the formerly unknown, potentially useful knowledge, rules, or models [1] from a large set of data. It can be observed in insurance agencies [2,3], web mining [2,4], financial institutes [2,5] and marketing contexts [2,6,7] This application is obtained by the open use of data with presupposition of data mining and knowledge discovery. Supplier-X will build monopoly without lowering the price In such aspect, the released database is dreadful for supermarket.”. XML association rules are generated over horizontally partitioned data through apriori algorithm
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