Hiding Sensitive The purpose of the conservative association rules is to minimize the risk of disclosure of shared information to external parties. In this paper, we proposed a PPDM model for XML association rules (XAR). The proposed model identifies the most likely element defined as sensitive to modify the original data source with greater accuracy and reliability. Such reliability has not been addressed before in the literature in any type of methodology used in the PPDM domain and in particular in XML association rule extraction. Therefore, the significance of the suggested model establishes and opens a new dimension for academia to control sensitive information in a more uncompromising line of attack. Keywords: XAR, PPDM, K2 algorithm, Bayesian network, association rules. INTRODUCTION In data mining, trends and patterns are identified across a large data set to uncover knowledge. In such analysis, there are varieties of algorithms for knowledge extraction such as clustering, classification and association rule extraction. Therefore, association rules mine a domain to provide knowledge about complex data. Furthermore, the basis of the discovered association rules is usually determined by the minimum support s% and minimum confidence c% to represent the transactional elements in database D. Therefore, it has the implication of the form AB, where A is the antecedent and B is the consequent. The problem with such a rule view is the disclosure of sensitive information to the outside when data is shared. From here emerges the protection of privacy in data mining (PPDM) related to the rules of the association. In PPDM, sensitive information is with...... middle of paper ......066-1395, IEEE Computer Society Washington, DC, USA[ 7]. M. Atallah, E. Bertino, A. Elmagarmid, M. Ibrahim, V. Verykios, “Disclosure Limitation of Sensitive Rules”, Page:45-52, Year of publication: 1999, ISBN:0-7695-0453-1, IEEE Computer Society, Washington, DC, USA[8]. Gregory F. Cooper and Edward Herskovits. A Bayesian method for inducing probabilistic networks from data. Mach. Learning., 9(4):309{347, 1992.[9]. R. Agralwal, T. Imielinski and A. Swami. Mining associations between itemsets in large databases. In P. Buneman and S. Jajodia, editors, SIGMOD93, pages 207-216, Washington, DC, USA, May 1993[10]. O. Doguc and J.E. Ramirez-Marquez “A generic method for estimating system reliability using Bayesian networks,” in proc. Reliability Engineering and System Safety, (2008)[11]. http://tunedit.org/repo/UCI/lymph.arff,DatasetAccessDate:31-03-2010

Topic > Hiding Sensitive The purpose of the conservative association rules is to minimize the risk of disclosure of shared information to external parties. In this paper, we proposed a PPDM model for XML association rules (XAR). The proposed model identifies the most likely element defined as sensitive to modify the original data source with greater accuracy and reliability. Such reliability has not been addressed before in the literature in any type of methodology used in the PPDM domain and in particular in XML association rule extraction. Therefore, the significance of the suggested model establishes and opens a new dimension for academia to control sensitive information in a more uncompromising line of attack. Keywords: XAR, PPDM, K2 algorithm, Bayesian network, association rules. INTRODUCTION In data mining, trends and patterns are identified across a large data set to uncover knowledge. In such analysis, there are varieties of algorithms for knowledge extraction such as clustering, classification and association rule extraction. Therefore, association rules mine a domain to provide knowledge about complex data. Furthermore, the basis of the discovered association rules is usually determined by the minimum support s% and minimum confidence c% to represent the transactional elements in database D. Therefore, it has the implication of the form AB, where A is the antecedent and B is the consequent. The problem with such a rule view is the disclosure of sensitive information to the outside when data is shared. From here emerges the protection of privacy in data mining (PPDM) related to the rules of the association. In PPDM, sensitive information is with...... middle of paper ......066-1395, IEEE Computer Society Washington, DC, USA[ 7]. M. Atallah, E. Bertino, A. Elmagarmid, M. Ibrahim, V. Verykios, “Disclosure Limitation of Sensitive Rules”, Page:45-52, Year of publication: 1999, ISBN:0-7695-0453-1, IEEE Computer Society, Washington, DC, USA[8]. Gregory F. Cooper and Edward Herskovits. A Bayesian method for inducing probabilistic networks from data. Mach. Learning., 9(4):309{347, 1992.[9]. R. Agralwal, T. Imielinski and A. Swami. Mining associations between itemsets in large databases. In P. Buneman and S. Jajodia, editors, SIGMOD93, pages 207-216, Washington, DC, USA, May 1993[10]. O. Doguc and J.E. Ramirez-Marquez “A generic method for estimating system reliability using Bayesian networks,” in proc. Reliability Engineering and System Safety, (2008)[11]. http://tunedit.org/repo/UCI/lymph.arff,DatasetAccessDate:31-03-2010