Paper Title
Hiding Individual Detail In Publishing Data Using Overlapping Slicing

Privacy preserving data publishing is one of the techniques which implement privacy on the collected large scale of data. For personal identification, data includes information and therefore publishing such data to third party or agencies may incur privacy threats, which include medical data. This thesis proposes a technique for applying privacy on data by using a method of overlapping slicing for handling high dimensional data. Many anonymization techniques were designed for privacy preservation to publish data. Data Publication has shown that generalization losses lot of information. For large scale of data, Bucketization does not prevent membership disclosure. This research partitions the attribute into more than one column in such a way that it can protect privacy by breaking association of uncorrelated data and preserve data utility. Keywords— Privacy, Overlapping Slicing, Privacy Preservation Data Publication.