Privacy Preservation of Sensitive Data Using Overlapping Slicing

Data that is not privacy preserved is as futile as obsolete data. Although security is imperative privacy is more important in micro data publishing. Anonymization technique, such as generalization, has been designed for privacy preserving micro data publishing. We present a novel technique called slicing. Slicing preserves data utility and can be used for membership disclosure protection. Data privacy has gained a wide popularity. Even though the method like generalization is used, it gives protection against membership disclosure. Slicing was used for attribute disclosure protection and develop an algorithm for computing the sliced data. In the proposed system the privacy preservation is enhanced by providing slicing for identity protection. The proposed system combines generalization and slicing concept and perform the privacy preservation to overcome the defined problem and generate new approach which is called as Overlap Slicing. It will handle the two processing tasks in a single algorithm. Privacy preservation will be improved using this concept as well as will reduce memory space requirement. In general for privacy preservation of any data we apply the generalization technique or slicing technique to preserve the privacy and hide the data. The proposed system combines both the techniques for data preservation.