A novel framework for preserving privacy of data using correlation analysis

Privacy preserving data mining is a novel research direction in data mining. In recent years, with the rapid development in Internet, data storage and data processing technologies, privacy preserving data mining has drawn increasing attention. Today's globally networked society places great demand on the discrimination and sharing of person-specific data. This happens at a time when more and more historically public information is also electronically available. This results in making private sensitive information of individuals available such as name, phone numbers, date of birth, etc. A number of methods and techniques have been developed for privacy preserving. The Anonymization based approach has the advantage of being efficient enough to deal with large volume of datasets. The basic idea underlying this approach is to let the data owners publish some sanitized versions of their data e.g., via suppression and generalization. In this paper a correlation based framework has been proposed based on correlation rule mining. This framework lets data owners share with each other the knowledge extracted from their own private datasets, rather than to let the data owners publish any of their own private datasets. It is clear that such methods reduce the risk of identification with the use of public records, while reducing the accuracy of applications on the transformed data