Enhanced Algorithm for Data Privacy Preservation using Data Anonymization with Low Information

Data are scattered in public cloud to share among the stake holders which produces much concern over the protection of individual privacy. Without revealing the delicate information, publishing data in a public cloud is a challenging aspect. To reveal data to the public leads to the introduction of many models like kanonymity and l-diversity. These methods safeguard the data against the adversary by attaining the knowledge about the sensitive information. But, in these approaches there exists many shortcomings such as the information loss. The research focuses the issues only with the k-anonymity. An efficient framework is proposed for privacy preservation. Considering the information loss metrics, onedimensional quasi identifiers are focused and studied the properties of optimal solutions for k-anonymity. To generalize, an efficient multi-dimensional quasi iden tifiers using space mapping methods are proposed. These experimental evaluations prove that the proposed approaches are more efficient in terms of execution time and information loss.