The A hybrid decision tree model for high dimensional privacy preserving process

Abstract

The data mining system can help to identify the important hidden patterns for decision-making
in large datasets. Privacy preserving data mining (PDDM) has emerged as a critical area for
the sharing, decision-making and dissemination of confidential data. Preserving privacy is a
common data security model for protecting unauthorised access to individual decision patterns.
Because the distributed data of the individuals is processed by the third Party, the information
in digital networks is misused. Such information on privacy about businesses , industries and
persons must be encoded prior to publication or published. During the processing of data from
various sources, decision patterns based on standard data security protection models such as
Naïve Bayes, SVM and the models for the decision tree are very difficult to maintain. In
addition, the use of traditional models to fill sparse values is inefficient and inadequate for the
protection of privacy. A novel data security model was developed and applied on large data
sets in this paper. In this model a philtre based data protection scheme is used to cover decision
patterns with homomorphic encoding and decryption algorithm using the decision-tabo
classifier. In this scheme. Experimental findings demonstrated the high processing efficiency
of the proposed model relative to conventional data protection approaches of large-scale
datasets.