Research Area:  Machine Learning

Abstract:

Data is the main fuel of a successful machine learning model. A dataset may contain sensitive individual records e.g. personal health records, financial data, industrial information, etc. Training a model using sensitive data has become a growing privacy concern, especially when third-party cloud computing is involved. Trained models are also vulnerable to privacy attacks, which can lead to the leakage of sensitive information from the training data. This study is conducted to preserve the privacy of training data in the context of customer churn prediction modeling for the telecommunications industry (TCI). In this work, we propose a framework for privacy-preserving customer churn prediction (PPCCP) model in the cloud environment. We have proposed a novel approach which is a combination of Generative Adversarial Networks (GANs) and adaptive Weight-of-Evidence (aWOE). Synthetic data is generated from GANs, and aWOE is applied on the synthetic training dataset before feeding the data to the classification algorithms. Our experiments were carried out using nine different machine learning (ML) classifiers on three openly accessible datasets from the telecommunication sector. We then evaluated the performance using six commonly employed evaluation metrics. In addition, statistical significance tests and a privacy analysis were performed. The training and prediction processes achieve data privacy, and the prediction classifiers achieve high prediction performance (87.1% in terms of F-Measure for GANs-aWOE based Naïve Bayes model). In contrast to earlier studies, our suggested approach demonstrates a prediction enhancement of up to 28.9% and 27.9% in terms of accuracy and F-measure, respectively.

Keywords:  

Author(s) Name:  Joydeb Kumar Sana , M. Sohel Rahman , M. Saifur Rahman

Publisher name:  ScienceDirect

DOI:  10.1016/j.engappai.2025.112514

Volume Information:  Volume 162, (2025)

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S095219762502545X