AI/ML FOR DATA PRIVACY AND ENCRYPTION IN CLOUD COMPUTING

As cloud computing becomes increasingly pervasive, ensuring data privacy and security remains a critical concern. Artificial intelligence (AI) and machine learning (ML) offer promising solutions for enhancing data privacy and developing advanced encryption techniques in cloud environments. This review article explores how AI and ML are applied to improve data privacy, including the development of intelligent encryption methods, privacy-preserving algorithms, and automated data protection mechanisms. We examine various approaches, such as homomorphic encryption, secure multi-party computation, and differential privacy, and assess their integration with AI/ML technologies. The article provides an overview of current research, evaluates the effectiveness of different techniques, and discusses the trade-offs involved. It concludes with a discussion on future trends and potential areas for further research in leveraging AI/ML for data privacy and encryption in cloud computing.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-08-15
DOI
https://doi.org/10.5281/zenodo.13324414
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
Field-Weighted Citation Impact
0.00
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article

AI/ML FOR DATA PRIVACY AND ENCRYPTION IN CLOUD COMPUTING

Zenodo (CERN European Organization for Nuclear Research)
Privacy-Preserving Technologies in Data
article

AI/ML FOR DATA PRIVACY AND ENCRYPTION IN CLOUD COMPUTING

article en

Abstract

As cloud computing becomes increasingly pervasive, ensuring data privacy and security remains a critical concern. Artificial intelligence (AI) and machine learning (ML) offer promising solutions for enhancing data privacy and developing advanced encryption techniques in cloud environments. This review article explores how AI and ML are applied to improve data privacy, including the development of intelligent encryption methods, privacy-preserving algorithms, and automated data protection mechanisms. We examine various approaches, such as homomorphic encryption, secure multi-party computation, and differential privacy, and assess their integration with AI/ML technologies. The article provides an overview of current research, evaluates the effectiveness of different techniques, and discusses the trade-offs involved. It concludes with a discussion on future trends and potential areas for further research in leveraging AI/ML for data privacy and encryption in cloud computing.

Zenodo (CERN European Organization for Nuclear Research)
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Privacy-Preserving Technologies in Data
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