Abstract
Conference Title: 2018 IEEE International Conference on Big Data (Big Data) Conference Start Date: 2018, Dec. 10 Conference End Date: 2018, Dec. 13 Conference Location: Seattle, WA, USA The security management of databases involves complex issues for the companies. The complexity of security measure needs to apply increases with the most complex database. The statistical queries associated with statistical database uses the aggregate functions such as min, max, average and count to provide statistical information about entries. The data vulnerabilities raise the security issues because of a clever combination of statistical queries to obtain confidential information for the individual entry. Undeniably, malpractices of user allow taking advantage of the aggregate functions and compromising the confidentiality. The statistical interference analysis poses significant risk for the security database system. This research proposes intelligent framework which is useful to hide sensitive information in case of combination of statistical queries. Firstly, the framework is useful to define the sensitive information for the decision making of the administrator to set some rules. Secondly, the framework explores the relationship between sensitive and other attributes in case of rule discrimination based on attribute-orientation and thus allows making a decision on attributes useful to drive confidential information.