  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.
