A Novel Data Anonymization Technique for Privacy Preservation of Data Publishing

Most enterprises are actively collecting and storing data in large databases. Many of them have recognized the potential value of data as an information source for making business decisions. Privacy preservation is important one for publishing the information containing individual specific records. In generally information about individual’s records will violate the privacy. So many techniques have been introduced for preserving the privacy. The existing systems have been designed with the anonymization techniques of generalization and bucketization. Those methods were revealed the privacy of individual data to the adversaries. Generalization involved considerable loss of the data and bucketization method does not protection from membership disclosure and there is no clear separation between sensitive attributes and quasi identifier attributes. The proposed system of Slicing with Tuple grouping algorithm. Partitioned The data both horizontally and vertically. It provides better information utility than generalization and protection from membership disclosure and also can handle in high dimensional data. The Government agencies, organization and companies are shared the data and publication of the data for research purposes. This paper focuses on effective method that can be used for providing better data utility and can handle high level dimensional data.