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A Blockchain-Based Verifiable User Data Access Control Policy for Secured Cloud Data Storage.

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  • 1School of Computer Science, Hunan Institute of Technology, Hengyang 421002, China.

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This summary is machine-generated.

This study introduces a novel blockchain-based access control system for securing sensitive health data in cloud storage. The system enhances data security and privacy through verifiable user data policies, improving resilience against attacks.

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Area of Science:

  • Computer Science
  • Information Security
  • Health Informatics

Background:

  • Information systems require robust access control for sensitive data, privacy, and defense.
  • Traditional authentication and authorization rely on user levels and resource confidentiality.
  • Blockchain technology offers immutable, distributed data logging for enhanced security.

Purpose of the Study:

  • To propose a blockchain-based verifiable user data access control policy for securing cloud data storage.
  • To apply this system to big data in healthcare, ensuring classified access policies.
  • To evaluate the system's resilience against specific security threats.

Main Methods:

  • Development of a blockchain-based access control policy for cloud data.
  • Implementation of classified access policies for secure resource management.
  • System evaluation through a case study focusing on resilience to Eclipse attacks and routing table poisoning.

Main Results:

  • The proposed system demonstrates a method for verifiable user data access control using blockchain.
  • The system is designed to secure big data in healthcare environments.
  • Case study analysis provides insights into the system's resilience against sophisticated network attacks.

Conclusions:

  • Blockchain technology can be effectively utilized to create secure, verifiable access control policies for cloud data.
  • The proposed system offers a promising approach for enhancing the security and privacy of big data in healthcare.
  • Further research should explore broader applications and advanced threat models.