CLASSIFICATION OF SEMANTICALLY SECURED ENCRYPTED RELATIONALDATA USING DATAMINING TECHNIQUES
Publication Date : 31/03/2016
Exchanging and publishing data are becoming an inherent part of business and academic practice, which in many areas that have been obtained after exorbitant and challenging procedures, that can provide detectable evidence for the legal ownership of a shared dataset, without the mutual consessions and its usability under a wide range of machine learning, mining, and search operations. The algorithms also preserve great significant properties of the dataset, which are important for mining operations, and so giving warranty for both right protection and utility preservation. The project considers a definite-protection statergy based on watermarking. Watermarking may control the original distance graph. The proposed watermarking methodology which safeguard the related data that are far apart. This leads to preservation of any mining operation that depends on the ordering of distances between objects. It proves fundamental lower and upper hops on the distance between objects post-watermarking.In particular, it establishes a certain limits for equally dimensed property.This analysis used to plan fast algorithms for NN-preserving watermarking that unpleasant vast search space.
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