A Framework for an Ontology-Based Data-Gleaning and Agent Based Intelligent Decision Support PPDM System Employing Generalization Technique for Health Care

---The obligatory to anticipate the privacy benefits of heavy downpour of monsoon rain from the firmament clouds of Privacy Preserving Data Mining (PPDM) Techniques have recently grown leaps and bounds. The desiccated users & miners look for the petite clemency from these little heavens in the form of a framework with PPDM Techniques. In this paper we have developed two things namely, the ontology based data gleaning system, the gleaned data is sent to a PPDM system which has an in-built generalization privacy technique and the agent-based intelligent decision support system. The primary report is on the implementation of existing generalized framework with alternate technology (i.e. implementation using Natural language processing instead of heuristic based method). Our Data Gleaning system will also allow new algorithms and ideas to be incorporated into a data extraction system. Extraction of information from semistructured or unstructured documents is a useful yet complex task. Ontologies can achieve a high degree of accuracy in data extraction system while maintaining resiliency in the face of document changes. Ontologies do not, however, diminish the complexity of a dataextraction system. As research in the field progress, the need for a modular data-extraction system that decouples the associated processes continues to grow. We also propose a generalization conceptual framework in this paper, where we guide the extracted data from the data Gleaning system to the generalization framework. The QI Generalization technique in the generalized framework is used to visor sensitive information and then publishes the privacy preserved data for knowledge discovery. Keywords—Ontology; Data extraction; generalization; privacy preservation.