  A large amount of data and applications need to be shared with various
parties and stakeholders in the cloud environment for storage, computation, and
data utilization. Since a third party operates the cloud platform, owners
cannot fully trust this environment. However, it has become a challenge to
ensure privacy preservation when sharing data effectively among different
parties. This paper proposes a novel model that partitions data into sensitive
and non-sensitive parts, injects the noise into sensitive data, and performs
classification tasks using k-anonymization, differential privacy, and machine
learning approaches. It allows multiple owners to share their data in the cloud
environment for various purposes. The model specifies communication protocol
among involved multiple untrusted parties to process owners data. The proposed
model preserves actual data by providing a robust mechanism. The experiments
are performed over Heart Disease, Arrhythmia, Hepatitis, Indian-liver-patient,
and Framingham datasets for Support Vector Machine, K-Nearest Neighbor, Random
Forest, Naive Bayes, and Artificial Neural Network classifiers to compute the
efficiency in terms of accuracy, precision, recall, and F1-score of the
proposed model. The achieved results provide high accuracy, precision, recall,
and F1-score up to 93.75%, 94.11%, 100%, and 87.99% and improvement up to 16%,
29%, 12%, and 11%, respectively, compared to previous works.
