A Novel Blockchain and Bi-Linear Polynomial-Based QCP-ABE Framework for Privacy and Security over the Complex Cloud...

Area of Science:

• Computer Science
• Information Security
• Cloud Computing

Background:

• Internet of Things (IoT) devices have limited resources, leading to outsourcing large-scale data to the cloud.
• Cloud computing poses risks to user privacy and data security due to its inherent unreliability.
• The exponential growth of cloud and grid data necessitates robust protection mechanisms for large datasets.

Purpose of the Study:

• To enhance cloud data security and maintain user privacy for large-scale datasets.
• To address the limitations of existing Attribute-Based Encryption (ABE) techniques for cloud data.
• To propose a novel method integrating blockchain and chaotic quantum hashing for secure data management.

Main Methods:

• Implementation of a dynamic non-linear polynomial chaotic quantum hash technique on a secure blockchain model.
• Utilizing a dynamic non-polynomial chaotic map function for key initialization, encryption, and decryption based on customer attributes.
• Processing both organized and unorganized massive clinical data for corroboration and encoding.

Main Results:

• The proposed method demonstrates high precision, exceeding 90% in bit change accuracy.
• Achieved over 95% precision in dynamic key generation, encipherment, and decipherment time.
• Real-time simulations confirm the effectiveness and efficiency of the proposed security standard.

Conclusions:

• The dynamic non-linear polynomial chaotic quantum hash technique on a blockchain offers a significant improvement in cloud data security and user privacy.
• The method effectively handles massive, diverse datasets, ensuring reliable data protection.
• This approach provides a robust solution for the growing challenges of securing large-scale cloud data.