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The distributed database environment supports partitioned database management operations. The partitioned data values are stored and maintained in different databases. Horizontal and vertical partitions are managed under the data providers or parties. Two or more parties have their own private data under the distributed environment. The parties can collaborate to calculate any function on the union of their data. Association rule mining techniques are used to fetch frequent patterns. Centralized and distributed rule mining models are applied to discover the frequent patterns under the distributed environment. Trusted nodes are used to perform rule mining in centralized environment. Privacy preserved data mining techniques are adapted to perform the knowledge discovery process with sensitive attribute protection mechanism. Anonymization methods are applied to secure the sensitive attributes I the public data values. Privacypreserved mining is performed on vertically partitioned databases under different data owners. The association rule mining process is carried out on collective data sets. Homomorphic encryption scheme and secure comparison scheme are adapted to ensure the data privacy. The rule mining operations are performed under the Cloud aided environment. Cloud aided framework is constructed to support data sharing and mining on outsourced data from multiple data owners. Horizontal and vertical partition based rule mining operations are supported in the system. All the partitioned data values are collected and integrated by the cloud server environment. The rule mining operations are carried out under the cloud server environment. Resource scheduling is integrated for the data and tasks under the Cloud Server. Data leakage control is improved with utility analysis mechanism. The sensitive attributes are protected with
K-anonymity technique. Data communication security is ensured with the RSA algorithm.

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How to Cite
K.Dhivyalakshmi, V.Kaviya, N.Kaviyarasu, & L.Viji. (2017). Privacy and Security Ensured Rule Mining under Partitioned Databases . International Journal of Intellectual Advancements and Research in Engineering Computations, 5(1), 835–841. Retrieved from