Contemporary data management systems search through enormous datasets to find patterns and correlations that were previously undiscovered in addition to storing and retrieving data. The need for computer applications and data mining software is rising as a result of how quickly new technologies are being developed. To ensure that all calculations lead to the same result, the necessary software and tools need to work with remote databases. However, because of legal restrictions and the necessity for a competitive edge, distributed data mining presents privacy concerns. Experts in the fields of big data, cyber security, and data mining are therefore motivated to study more. Researchers created Privacy-preserving Distributed Data Mining (PPDDM) to address the multi-party computation problem, in which multiple users attempt to perform a data mining task cooperatively using their respective private data sets, in order to get around these limitations and benefit from these advantages. Participants discover only the outcomes of the data mining algorithm and their own inputs after finishing the exercise. The main objective behind this research was to provide a novel Privacy Preserving Data Mining approach for creating Decision Tree Classifiers utilizing data that has been vertically partitioned. The recommended PPDM approach is utilized towards build a conclusion tree classifier in Weka, and the results are compared to those from the well-established J48 method. This analysis employs accuracy and precision as its standards. Compared to the conventional approach, the suggested PPDM algorithm offers far greater accuracy and precision.