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.