Privacy-Preserving Customer Churn Prediction Model in the Context of Telecommunication Industry
- URL: http://arxiv.org/abs/2411.01447v1
- Date: Sun, 03 Nov 2024 06:08:59 GMT
- Title: Privacy-Preserving Customer Churn Prediction Model in the Context of Telecommunication Industry
- Authors: Joydeb Kumar Sana, M Sohel Rahman, M Saifur Rahman,
- Abstract summary: We propose a framework for privacy-preserving customer churn prediction 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)
- Score: 1.0428401220897083
- License:
- 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 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\"{\i}ve 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.
Related papers
- Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning [22.705411388403036]
This paper develops a novel personalized federated learning algorithm.
Each client constructs a personalized model by combining a locally fine-tuned model with multiple federated models.
Theoretical analysis and experiments on real datasets corroborate the effectiveness of this approach.
arXiv Detail & Related papers (2024-10-28T21:20:51Z) - Learning Augmentation Policies from A Model Zoo for Time Series Forecasting [58.66211334969299]
We introduce AutoTSAug, a learnable data augmentation method based on reinforcement learning.
By augmenting the marginal samples with a learnable policy, AutoTSAug substantially improves forecasting performance.
arXiv Detail & Related papers (2024-09-10T07:34:19Z) - F-FOMAML: GNN-Enhanced Meta-Learning for Peak Period Demand Forecasting with Proxy Data [65.6499834212641]
We formulate the demand prediction as a meta-learning problem and develop the Feature-based First-Order Model-Agnostic Meta-Learning (F-FOMAML) algorithm.
By considering domain similarities through task-specific metadata, our model improved generalization, where the excess risk decreases as the number of training tasks increases.
Compared to existing state-of-the-art models, our method demonstrates a notable improvement in demand prediction accuracy, reducing the Mean Absolute Error by 26.24% on an internal vending machine dataset and by 1.04% on the publicly accessible JD.com dataset.
arXiv Detail & Related papers (2024-06-23T21:28:50Z) - Data Shapley in One Training Run [88.59484417202454]
Data Shapley provides a principled framework for attributing data's contribution within machine learning contexts.
Existing approaches require re-training models on different data subsets, which is computationally intensive.
This paper introduces In-Run Data Shapley, which addresses these limitations by offering scalable data attribution for a target model of interest.
arXiv Detail & Related papers (2024-06-16T17:09:24Z) - Prompt Public Large Language Models to Synthesize Data for Private On-device Applications [5.713077600587505]
This paper investigates how large language models (LLMs) trained on public data can improve the quality of pre-training data for the on-device language models trained with DP and FL.
The model pre-trained on our synthetic dataset achieves relative improvement of 19.0% and 22.8% in next word prediction accuracy.
Our experiments demonstrate the strengths of LLMs in synthesizing data close to the private distribution even without accessing the private data.
arXiv Detail & Related papers (2024-04-05T19:14:14Z) - Approximate, Adapt, Anonymize (3A): a Framework for Privacy Preserving
Training Data Release for Machine Learning [3.29354893777827]
We introduce a data release framework, 3A (Approximate, Adapt, Anonymize), to maximize data utility for machine learning.
We present experimental evidence showing minimal discrepancy between performance metrics of models trained on real versus privatized datasets.
arXiv Detail & Related papers (2023-07-04T18:37:11Z) - Conformal prediction for the design problem [72.14982816083297]
In many real-world deployments of machine learning, we use a prediction algorithm to choose what data to test next.
In such settings, there is a distinct type of distribution shift between the training and test data.
We introduce a method to quantify predictive uncertainty in such settings.
arXiv Detail & Related papers (2022-02-08T02:59:12Z) - DER Forecast using Privacy Preserving Federated Learning [0.0]
A distributed machine learning approach, Federated Learning, is proposed to carry out DER forecasting using a network of IoT nodes.
We consider a simulation study which includes 1000 DERs, and show that our method leads to an accurate prediction of preserve consumer privacy.
arXiv Detail & Related papers (2021-07-07T14:25:43Z) - An Analysis of the Deployment of Models Trained on Private Tabular
Synthetic Data: Unexpected Surprises [4.129847064263057]
Diferentially private (DP) synthetic datasets are a powerful approach for training machine learning models.
We study the effects of differentially private synthetic data generation on classification.
arXiv Detail & Related papers (2021-06-15T21:00:57Z) - Representative & Fair Synthetic Data [68.8204255655161]
We present a framework to incorporate fairness constraints into the self-supervised learning process.
We generate a representative as well as fair version of the UCI Adult census data set.
We consider representative & fair synthetic data a promising future building block to teach algorithms not on historic worlds, but rather on the worlds that we strive to live in.
arXiv Detail & Related papers (2021-04-07T09:19:46Z) - Omni-supervised Facial Expression Recognition via Distilled Data [120.11782405714234]
We propose omni-supervised learning to exploit reliable samples in a large amount of unlabeled data for network training.
We experimentally verify that the new dataset can significantly improve the ability of the learned FER model.
To tackle this, we propose to apply a dataset distillation strategy to compress the created dataset into several informative class-wise images.
arXiv Detail & Related papers (2020-05-18T09:36:51Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.