# A Privacy-Preserving Model based on Differential Approach for Sensitive Data in Cloud Environment

arXiv: https://arxiv.org/abs/2212.12534 · HTML: https://mlsanity.com/paper/2212.12534/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2022.

## Built on by (most cited first, 3 of 3)

- [MAIDS: Malicious Agent Identification-based Data Security Model for Cloud Environments](https://mlsanity.com/paper/2412.14490/) (2024)
- [A Hybrid Proactive And Predictive Framework For Edge Cloud Resource Management](https://mlsanity.com/paper/2511.16075/) (2025)
- [An Optimized Machine Learning Classifier for Detecting Fake Reviews Using Extracted Features](https://mlsanity.com/paper/2511.21716/) (2025)

## Abstract (arXiv)

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.