Quasi Attribute Utility Enhancement (QAUE)- A Hybrid Method for PPDP

Abstract

The data analytics has become prominent for today’s world because it is defined as the methodology of investigating data sets in order to draw conclusion about the information it contain. The Data Mining is a key part of Data Analytics because it has techniques and tools which help to explore knowledge which is hidden in data. The outcome of data analytics is very crucial to Business organizations because it helps in decision making process. In Data Analytics there are two roles which are very prominent and they are Data publisher and Data Analyzer. Data Publisher is the one who provides data for analytics which is collected from heterogeneous sources. Data Analyzer receives data from Data publisher and uses for data analytics. The main issue involves here is data privacy, which is concerned with the proper treatment of data i.e. approval, discern and regulations. A separate field called PPDP- Privacy Preserving Data Publishing mainly concentrates on how data is shared, used by data analysts and it may be implicit or explicit to organizations (third party) such that it should be safer from untrusted people and attacks. The PPDP offers several approaches to publish data in safe manner and supports data utility, but there is a need of domain specific privacy concern because privacy needs are different based on the domain and in mean time how data is utilized. In the paper a hybrid approach is proposed to preserve data privacy in concern with data publisher which supports domain specific data privacy and utility