Advanced Private Cloud Security and Privacy Preservation Through the Integration of Machine Learning and Cryptography

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Year : July 23, 2024 at 12:29 pm | [if 1553 equals=””] Volume :02 [else] Volume :02[/if 1553] | [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] : 01 | Page : 1-10

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Padma Bhogaraju, D. Deepthi,

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  1. Associate Professor,, Assistant Professor, Gayatri Vidya Parishad College for Degree and PG Courses (Autonomous), Visakhapatnam,, Rayapati Venkata Rangarao and Jagarlamudi Chandramouli (RVR&JC) College of Engineering (Autonomous), Guntur, Andhra Pradesh,, Andhra Pradesh, India, India
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Abstract

nIn modern technological landscapes, private cloud security is of paramount concern due to the ever-increasing volume and complexity of cyber threats. This research work explores the integration of machine learning and cryptography to enhance security within private cloud environments. This study aims to mitigate vulnerabilities that may compromise data integrity, confidentiality, and availability in private cloud infrastructures by using machine learning algorithms and strong cryptography. By detecting anomalous cloud patterns and behaviours, machine learning algorithms enable proactive threat detection. Machine learning models can distinguish normal network activity from malicious incursions by analysing large datasets, improving early threat detection and response. By using cryptographic protocols like homomorphic encryption and multi-party computation, private clouds can protect sensitive data while maintaining computational capabilities. This study examines how machine learning and cryptography can strengthen private cloud security against sophisticated cyber-attacks. Industry practitioners, academic scholars, and cyber security professionals seeking innovative private cloud asset protection strategies will benefit from the findings. As organizations move to private cloud infrastructures for flexibility and scalability, advanced security paradigms are needed to adapt to changing threats. This study suggests that combining cutting-edge technologies like machine learning and cryptography can enhance private cloud defences, both theoretically and practically.

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Keywords: Homomorphic encryption, multi-party computations, machine learning models, cryptography and private cloud Security

n[if 424 equals=”Regular Issue”][This article belongs to International Journal of Advanced Control and System Engineering(ijacse)]

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[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in International Journal of Advanced Control and System Engineering(ijacse)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

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How to cite this article: Padma Bhogaraju, D. Deepthi. Advanced Private Cloud Security and Privacy Preservation Through the Integration of Machine Learning and Cryptography. International Journal of Advanced Control and System Engineering. July 22, 2024; 02(01):1-10.

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How to cite this URL: Padma Bhogaraju, D. Deepthi. Advanced Private Cloud Security and Privacy Preservation Through the Integration of Machine Learning and Cryptography. International Journal of Advanced Control and System Engineering. July 22, 2024; 02(01):1-10. Available from: https://journals.stmjournals.com/ijacse/article=July 22, 2024/view=0

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References

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[if 424 not_equal=””]Regular Issue[else]Published[/if 424] Subscription Book Review

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Volume 02
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 01
Received May 21, 2024
Accepted June 25, 2024
Published July 22, 2024

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