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Related Experiment Videos

Customer churn prediction in privacy-preserving HashCode-based security abstractions.

Asmaa Munshi1

  • 1College of Computer Science and Engineering, University of Jeddah, Jeddah, 21577, Saudi Arabia. ammunshi@uj.edu.sa.

Scientific Reports
|May 17, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a privacy-preserving method for customer churn prediction using HashCode security. Machine learning models, especially the Multilayer Perceptron (MLP), accurately identify churners while protecting customer identity.

Related Experiment Videos

Area of Science:

  • Data Security and Privacy
  • Machine Learning Applications
  • Customer Relationship Management

Background:

  • Customer churn is a significant concern for businesses, impacting revenue and market share.
  • Existing churn prediction models often require sensitive customer data, raising privacy issues.
  • Regulatory compliance (e.g., GDPR, CCPA) necessitates robust data protection measures.

Purpose of the Study:

  • To develop a privacy-by-design security abstraction for customer churn statistics.
  • To evaluate the effectiveness of various machine learning (ML) and deep learning (DL) models in predicting churn with enhanced privacy.
  • To demonstrate a deployable, regulation-compliant architecture for behavior-driven churn analytics.

Main Methods:

  • Implemented a HashCode-based security abstraction to anonymize customer data.
  • Trained and evaluated four models: Logistic Regression (SGD), Random Forest, XGBoost, and Multilayer Perceptron (MLP).
  • Utilized behavioral, transactional, and temporal customer data for model training and validation.

Main Results:

  • The Multilayer Perceptron (MLP) achieved the highest performance with 80.0% accuracy, 0.802 precision, 0.802 recall, 0.802 F1-score, and 0.825 AUC.
  • SGD Logistic Regression showed strong sensitivity to churners (77.5% accuracy, 0.812 recall).
  • Random Forest demonstrated good generalization (77.0% accuracy, 0.794 AUC), while XGBoost performed less effectively on this dataset.

Conclusions:

  • Privacy-preserving churn prediction is achievable using behavior-driven analytics and robust security abstractions.
  • The HashCode-based approach safeguards identity without compromising analytical integrity.
  • The MLP model offers superior performance for churn prediction in privacy-conscious environments.