Intelligent Data Management and Security in Cloud Computing

Area of Science:

• Computer Science
• Information Security
• Cryptography

Background:

• Current data protection methods face challenges in securing sensitive information and data services.
• The need for advanced protocols for confidential data sharing is critical in distributed environments.
• Existing solutions may not adequately address the complexities of multi-level data management and protection.

Purpose of the Study:

• To present novel techniques for secret data management and protection, focusing on securing data services.
• To introduce information-sharing protocols designed for confidential data, presented algorithmically.
• To develop intelligent linguistic threshold schemes combining cryptographic and linguistic methods for enhanced security.

Main Methods:

• Development of algorithmic forms for data-sharing protocols to prevent unauthorized acquisition.
• Application of cryptographic threshold techniques to split secrets among trustees.
• Integration of linguistic methods for describing shared secrets, creating intelligent linguistic threshold schemes.
• Utilizing formal graphs for secret representation, division, and transmission over distributed networks.

Main Results:

• Demonstration of new protocols for securing data services at various management levels.
• Successful integration of cryptographic thresholding and linguistic methods into novel security schemes.
• Feasibility assessment of applying these protocols to cloud management processes and other complex environments.

Conclusions:

• Intelligent linguistic threshold schemes offer a new class of protocols for advanced data protection.
• The presented techniques enhance the security of data services and confidential information sharing.
• The innovative approach using formal graphs and distributed networks provides a robust framework for secret management.