# A Privacy-Preserving Outsourced Data Model in Cloud Environment

arXiv: https://arxiv.org/abs/2211.13542 · HTML: https://mlsanity.com/paper/2211.13542/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 72 references that are in the map.
- First posted on arXiv 24 Nov 2022.

## Builds on (most cited first, 10 of 10)

- [Privacy-Preserving Deep Learning via Weight Transmission](https://mlsanity.com/paper/1809.03272/) (2018)
- [A Secure and Multi-objective Virtual Machine Placement Framework for Cloud Data Centre](https://mlsanity.com/paper/2107.13502/) (2021)
- [Credit Card Fraud Detection using Machine Learning: A Study](https://mlsanity.com/paper/2108.10005/) (2021)
- [A Holistic View on Data Protection for Sharing, Communicating, and Computing Environments: Taxonomy and Future Directions](https://mlsanity.com/paper/2202.11965/) (2022)
- [workload forecasting and resource management models based on machine learning for cloud computing environments](https://mlsanity.com/paper/2106.15112/) (2021)
- [Relating CNNs with brain: Challenges and findings](https://mlsanity.com/paper/2108.09768/) (2021)
- [A study on Machine Learning Approaches for Player Performance and Match Results Prediction](https://mlsanity.com/paper/2108.10125/) (2021)
- [A Discussion and Comparative Study on Security and Privacy of Smart Meter Data](https://mlsanity.com/paper/2111.09227/) (2021)
- [A Comprehensive Vision on Cloud Computing Environment: Emerging Challenges and Future Research Directions](https://mlsanity.com/paper/2207.07955/) (2022)
- [Efficient Resource Management in Cloud Environment](https://mlsanity.com/paper/2207.12085/) (2022)

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

- [Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey](https://mlsanity.com/paper/2505.01821/) (2025)
- [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)

Nowadays, more and more machine learning applications, such as medical diagnosis, online fraud detection, email spam filtering, etc., services are provided by cloud computing. The cloud service provider collects the data from the various owners to train or classify the machine learning system in the cloud environment. However, multiple data owners may not entirely rely on the cloud platform that a third party engages. Therefore, data security and privacy problems are among the critical hindrances to using machine learning tools, particularly with multiple data owners. In addition, unauthorized entities can detect the statistical input data and infer the machine learning model parameters. Therefore, a privacy-preserving model is proposed, which protects the privacy of the data without compromising machine learning efficiency. In order to protect the data of data owners, the epsilon-differential privacy is used, and fog nodes are used to address the problem of the lower bandwidth and latency in this proposed scheme. The noise is produced by the epsilon-differential mechanism, which is then added to the data. Moreover, the noise is injected at the data owner site to protect the owners data. Fog nodes collect the noise-added data from the data owners, then shift it to the cloud platform for storage, computation, and performing the classification tasks purposes.