# Privacy-Preserving Customer Churn Prediction Model in the Context of Telecommunication Industry

arXiv: https://arxiv.org/abs/2411.01447 · HTML: https://mlsanity.com/paper/2411.01447/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 73 references that are in the map.
- First posted on arXiv 3 Nov 2024.

## Builds on (most cited first, 13 of 13)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)
- [The Discrete Gaussian for Differential Privacy](https://mlsanity.com/paper/2004.00010/) (2020)
- [DP-CGAN: Differentially Private Synthetic Data and Label Generation](https://mlsanity.com/paper/2001.09700/) (2020)
- [Generative Models for Effective ML on Private, Decentralized Datasets](https://mlsanity.com/paper/1911.06679/) (2019)
- [Context-Aware Generative Adversarial Privacy](https://mlsanity.com/paper/1710.09549/) (2017)
- [Privacy-Preserving Distributed Deep Learning for Clinical Data](https://mlsanity.com/paper/1812.01484/) (2018)
- [Routes for breaching and protecting genetic privacy](https://mlsanity.com/paper/1310.3197/) (2013)
- [Social Network Analytics for Churn Prediction in Telco: Model Building, Evaluation and Network Architecture](https://mlsanity.com/paper/2001.06701/) (2020)
- [Data transformation based optimized customer churn prediction model for the telecommunication industry](https://mlsanity.com/paper/2201.04088/) (2022)
- [Data Masking with Privacy Guarantees](https://mlsanity.com/paper/1901.02185/) (2019)

## Built on by (most cited first, 3 of 3)

- [Class-Aware Adaptive Differential Privacy in Deep Learning for Sensor-Based Fall Detection](https://mlsanity.com/paper/2605.01679/) (2026)
- [Patient Similarity Computation for Clinical Decision Support: An Efficient Use of Data Transformation, Combining Static and Time Series Data](https://mlsanity.com/paper/2506.07092/) (2025)
- [A Differentially Private Federated Proximal Optimization Framework for Customer Churn Prediction in Heterogeneous Federated Telecom Networks](https://mlsanity.com/paper/2609.12470/) (2026)

## Abstract (arXiv)

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 this sensitive data has become a new privacy concern when someone uses third-party cloud computing. Trained models also suffer privacy attacks which leads to the leaking of sensitive information of 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 eight 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 to presenting a data privacy analysis, we also performed a statistical significance test. 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 Na"ive 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.