Enhanced Robust and Reversible Watermarking for Supervised Learning Data

Large database have large data structure and have their large relational data for sharing for particular target or authenticate target but there is some other parties attack on data easily and use that data illegally. Attacker can gain ownership on that sharing data. While original data get modified and quality of data also degraded so this original data not useful for any extraction information system, it gives wrong data or reduced data. For that we used a system Reversible Watermarking which protects data from Attack of middle parties while sharing data and also preserve ownership of the data. quality of the data also preserve avoid the data tampering. Feature selection in RRW uses all combinations of features to calculate importance of the features. In in supervised learning, features importance depends on co-relation between Feature and class variable, there is no need to consider all combination in such case. Also RRW does not support non-numeric data. We introduced technique which works on nominal data and uses less features for calculation which enhance the speed and accuracy and performance of RRW.