Summary of Privacy-preserving Customer Churn Prediction Model in the Context Of Telecommunication Industry, by Joydeb Kumar Sana et al.
Privacy-Preserving Customer Churn Prediction Model in the Context of Telecommunication Industry
by Joydeb Kumar Sana, M Sohel Rahman, M Saifur Rahman
First submitted to arxiv on: 3 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework for privacy-preserving customer churn prediction (PPCCP) model in the cloud environment combines Generative Adversarial Networks (GANs) and adaptive Weight-of-Evidence (aWOE) to generate synthetic data. The aWOE is applied on the synthetic training dataset before feeding it to classification algorithms, ensuring data privacy while achieving high prediction performance (87.1% F-Measure for GANs-aWOE based Naïve Bayes model). This approach demonstrates a prediction enhancement of up to 28.9% and 27.9% in terms of accuracy and F-measure, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to keep customer information private when using cloud computing for machine learning models. They used special techniques called Generative Adversarial Networks (GANs) and adaptive Weight-of-Evidence (aWOE) to create fake data that is similar to real customer data, but doesn’t actually contain any personal information. This helps protect customers’ sensitive information from being leaked. The team tested their approach using different machine learning algorithms and found it worked well, achieving high accuracy and keeping customer data private. |
Keywords
* Artificial intelligence * Classification * Machine learning * Synthetic data
