A Framework for Privacy-preserving AI Models in Cloud Computing: Challenges and Solutions

Year : 2024 | Volume : 11 | Issue : 03 | Page : 1 12
    By

    Harshvardhan Chunawala,

  • Pratikkumar Chunawala,

  1. Cloud Infrastructure Architect, Amazon Web Services (AWS) – 10 Exchange Place, Jersey City, New Jersey, USA
  2. Principal Cloud Architect, Amazon Web Services (AWS) – 10 Exchange Place, Jersey City, New Jersey, USA

Abstract

The growing adoption of cloud computing for deploying artificial intelligence (AI) models has led to significant advancements in sectors such as healthcare, finance, and e-commerce. However, the integration of AI with cloud computing raises critical privacy concerns, particularly when handling sensitive data. This paper presents a comprehensive framework for implementing privacy-preserving AI models in cloud environments, addressing the unique challenges, and proposing effective solutions. The suggested framework employs advanced privacy-preserving methods, such as differential privacy, homomorphic encryption, and federated learning, to ensure that AI models function securely without jeopardizing data confidentiality. This study identifies key challenges associated with cloud-based AI models, such as data leakage, model inversion attacks, and the trade-off between privacy and model accuracy. The framework incorporates robust encryption methods to protect data during transmission and storage, while federated learning allows for decentralized training of AI models across multiple devices without sharing raw data. The paper also explores how to balance high model performance with strong privacy guarantees, emphasizing the need to optimize these techniques to meet industry standards. A series of experiments and simulations were conducted to evaluate the effectiveness of the proposed framework. The results show that the framework enhances data privacy while maintaining the accuracy and efficiency of AI models in cloud environments. The findings suggest that adopting such a framework can mitigate privacy risks, thereby fostering greater trust and adoption of AI-driven cloud services. This research contributes to the ongoing discourse on privacy in AI, offering practical insights and a solid foundation for future developments in privacy-preserving technologies within cloud computing.

Keywords: Privacy-preserving AI, cloud computing, differential privacy, homomorphic encryption, federated learning, data security, model accuracy, cloud-based AI models

[This article belongs to Journal of Operating Systems Development & Trends ]

How to cite this article:
Harshvardhan Chunawala, Pratikkumar Chunawala. A Framework for Privacy-preserving AI Models in Cloud Computing: Challenges and Solutions. Journal of Operating Systems Development & Trends. 2024; 11(03):1-12.
How to cite this URL:
Harshvardhan Chunawala, Pratikkumar Chunawala. A Framework for Privacy-preserving AI Models in Cloud Computing: Challenges and Solutions. Journal of Operating Systems Development & Trends. 2024; 11(03):1-12. Available from: https://journals.stmjournals.com/joosdt/article=2024/view=180714


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Regular Issue Subscription Review Article
Volume 11
Issue 03
Received 18/10/2024
Accepted 22/10/2024
Published 04/11/2024
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