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C4.5 algorithm is developed by Ross Quinlan which is the extension of ID3 algorithm used for generating a decision trees.Since the tree generated by C4.5 can be used for classification, so it's also referred to as statistical classifier.Even though the Random Decision Tree is used to avoid the information leakage there are some problems and issues related to privacy maintenance.When we try to instantiate more instances for one class it leads to ambiguity at the same time creating new classes more and more will increase the complexity in RDT. These problems can be resolved by using our C4.5 algorithm.We can have any number of nodes in a network, each node can create its own tree or class and each class can initiate many number of instances for a disseminated classification consuming secure amount or threshold homomorphic encryption. The main objective of this paper is to discuss the ideal nature of the C4.5 algorithm and how they support this algorithm to be utilized in various datamining process.

Machine learning algorithms are used immensely for performing most important computational tasks with the help of sample data sets.  Most of the cases Machine learning algorithms will provide best solution where the programming languages failed to produce viable and economically cost-effective results.  Huge volume of deterministic problems are addressed and tackled by using the available sample data sets.  Because of this now a days machine learning concepts are extensively used in computer science and many other fields.  But still we need to explore more to implement machine learning in a specific field such as network analysis, stock trading, spam filters, traffic analysis, real-time and non-real time traffic etc., which may not be available in text books.  Here I would like to discourse some of the key points that the machine learning researchers and practitioners can make use of them.  These include shortcomings and concerns also.