AbstractsComputer Science

Data-mining is a task of discovering significant/salient newlinepatterns/rules/results from a set of large amount of data stored in databases, newlinedata warehouses or in other information repositories. Eventhough the focus on newlinedata-mining technology has been on the discovery of general patterns (not on newlineany specific information regarding individuals) some data-mining applications newlinemay require access to individual s records having sensitive privacy data. Data newlinecontaining structured information on individuals is referred to as micro-data. newlineAbundance of recorded, personal information available in electronic form newlinecoupled with increasingly powerful data-mining tools, poses a threat to newlineprivacy and data security. The prime objective of this research is to find a newlinesolution to this problem. newlineEventhough, the identifying attributes are not published, some set newlineof attributes in a released table (called quasi identifiers) may be linked with newlineexternal data base leaking the sensitive data. To alleviate this problem, the so newlinecalled K-anonymity and L-diversity principles and their improved versions newlinehave been used popularly in the earlier research works. But, all such methods newlinesuffer from proximity and divergence breach considerations. Also, the choice newlineof generalization and diversity principles depends on the needs of underlying newlineapplication, that is, application specific. Further, in such methods, if the%%%