A study on micro data preservation using intersect carving

In terms of privacy for sensitive information anonymization techniques are used. Microdata clutches information about an individual along with sensitive information. Existing anonymization techniques are Bucketization, Generalization, Slicing and Overlap slicing. Generalization does not support high dimensional data and loses information. Bucketization does not suitable to preserve identity disclosure. The Slicing method delivers more secrecy and data efficacy than other existing methods but still, it has few limitations. To overcome the limitations a new technique called intersect carving is proposed. Intersect carving provides a high level of privacy and better data utility than slicing technique. In this method, attributes are duplicated in more than one row to obscure opponents and preserve identity disclosure.