A Differential Approach for Data and Classification Service based Privacy-Preserving Machine Learning Model in Cloud Environment

TL;DR

This paper introduces a differential privacy-based model for secure data sharing and classification in cloud environments, enabling multiple owners to collaboratively utilize machine learning while preserving privacy.

Contribution

It presents a novel differential privacy framework with a communication protocol for privacy-preserving machine learning in untrusted cloud settings.

Findings

Achieves up to 94% accuracy and 95% precision in experiments.

Improves classification metrics by up to 23.33% over existing methods.

Demonstrates effectiveness with Naive Bayes classifier on multiple datasets.

Abstract

The massive upsurge in computational and storage has driven the local data and machine learning applications to the cloud environment. The owners may not fully trust the cloud environment as it is managed by third parties. However, maintaining privacy while sharing data and the classifier with several stakeholders is a critical challenge. This paper proposes a novel model based on differential privacy and machine learning approaches that enable multiple owners to share their data for utilization and the classifier to render classification services for users in the cloud environment. To process owners data and classifier, the model specifies a communication protocol among various untrustworthy parties. The proposed model also provides a robust mechanism to preserve the privacy of data and the classifier. The experiments are conducted for a Naive Bayes classifier over numerous datasets to compute the proposed model efficiency. The achieved results demonstrate that the proposed model has high accuracy, precision, recall, and F1-score up to 94%, 95%, 94%, and 94%, and improvement up to 16.95%, 20.16%, 16.95%, and 23.33%, respectively, compared with state-of-the-art works.