Haina Storage: A Decentralized Secure Storage Framework Based on Improved Blockchain Structure

Core Concepts

A novel decentralized storage framework is proposed, which includes a Bi-direction Circular Linked Chain Structure (BCLCS) to improve data storage capacity and applicability, a Proof of Resources (PoR) decision model to reduce energy and time consumption and improve fairness, a Chain Structure Dynamic Locking Mechanism (CSDLM) to realize anti-traverse and access control, and a Bi-directional Data Access Mechanism (BDAM) to improve data access efficiency.

Abstract

The paper presents a novel decentralized storage framework with several key components:

Bi-direction Circular Linked Chain Structure (BCLCS):

Divides blocks into pointer domain and data domain to improve storage capacity and efficiency
Breaks the dependency in traditional blockchain to enable distributed storage

Chain Structure Dynamic Locking Mechanism (CSDLM):

Transforms the hash pointers to hide the correlation between data blocks
Prevents attackers from tracing the entire data chain

Proof of Resources (PoR) Decision Model:

Considers network environment as a key parameter in the storage node selection process
Reduces energy and time consumption, and improves fairness compared to existing models

Bi-directional Data Access Mechanism (BDAM):

Imitates DNA replication to enable bi-directional data access
Improves data access efficiency by 38% compared to traditional methods
The experimental results show that the proposed framework significantly improves the shortcomings of current decentralized storage solutions in terms of storage capacity, data security, and access efficiency.

Stats

The experimental code for this paper can be accessed at https://github.com/Zijian-Zhou/Haina_Storage_Exp
The engineering prototype developed for this paper is accessible at https://github.com/Zijian-Zhou/Haina_Storage

Quotes

"Although the decentralized storage technology based on the blockchain can effectively realize secure data storage on cloud services. However, there are still some problems in the existing schemes, such as low storage capacity and low efficiency."
"To address related issues, we propose a novel decentralized storage framework, which mainly includes four aspects: (1) we proposed a Bi-direction Circular Linked Chain Structure (BCLCS), which improves data's storage capacity and applicability in decentralized storage. (2) A Proof of Resources (PoR) decision model is proposed. By introducing the network environment as an essential evaluation parameter of storage right decision, the energy and time consumption of decision-making are reduced, and the fairness of decision-making is improved. (3) A chain structure dynamic locking mechanism (CSDLM) is designed to realize anti-traverse and access control. (4) A Bi-directional data Access Mechanism (BDAM) is proposed, which improves the efficiency of data access and acquisition in decentralized storage mode."

Key Insights Distilled From

Haina Storage

by Zijian Zhou,... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01606.pdf

Deeper Inquiries

How can the proposed framework be extended to support more advanced features, such as data versioning, access control, and incentive mechanisms?

To extend the proposed framework to support more advanced features, several enhancements can be considered:

Data Versioning: Implement a version control system within the framework to track changes made to data blocks. This can involve timestamping each block and maintaining a history of changes. Users can access and revert to previous versions of data blocks if needed.

Access Control: Introduce access control mechanisms to regulate who can read, write, or modify data blocks within the decentralized network. This can involve encryption keys, permissions, and authentication protocols to ensure data security and privacy.

Incentive Mechanisms: Incorporate incentive mechanisms such as token rewards or cryptocurrency payments for storage nodes that contribute resources to the network. This can incentivize nodes to participate actively and maintain the integrity of the decentralized storage framework.

By integrating these advanced features, the framework can offer enhanced data management capabilities, improved security, and increased participation from storage nodes.

How can the potential challenges and limitations of the BCLCS structure be addressed in future research?

The BCLCS structure may face challenges and limitations such as scalability issues, data integrity concerns, and performance bottlenecks. To address these in future research, the following strategies can be considered:

Scalability: Research can focus on optimizing the structure to handle a larger number of storage nodes and data blocks efficiently. This may involve exploring distributed storage techniques, sharding mechanisms, or parallel processing to improve scalability.

Data Integrity: Future research can enhance data verification and validation mechanisms within the BCLCS to ensure the integrity of stored data. Implementing checksums, digital signatures, and consensus algorithms can help maintain data consistency and prevent tampering.

Performance: Addressing performance bottlenecks by optimizing data retrieval, storage, and access processes. This can involve refining algorithms, reducing latency, and improving network communication to enhance the overall performance of the decentralized storage framework.

By tackling these challenges through innovative research and technological advancements, the BCLCS structure can evolve to meet the demands of secure and efficient decentralized storage systems.

How can the PoR decision model be further improved to better adapt to dynamic network conditions and storage node behaviors?

To enhance the PoR decision model for better adaptation to dynamic network conditions and storage node behaviors, the following improvements can be considered:

Dynamic Parameter Adjustment: Implement algorithms that dynamically adjust parameters such as storage thresholds, evaluation criteria, and network status weights based on real-time network conditions. This flexibility can help the model adapt to changing environments.

Machine Learning Integration: Integrate machine learning algorithms to analyze historical data, predict future network trends, and optimize decision-making processes. Machine learning can enhance the model's ability to learn and adapt to varying storage node behaviors.

Smart Contract Implementation: Utilize smart contracts on blockchain platforms to automate decision-making processes, enforce storage rules, and incentivize storage nodes based on their performance and contributions. Smart contracts can add transparency and efficiency to the PoR model.

By incorporating these enhancements, the PoR decision model can become more robust, adaptive, and efficient in managing decentralized storage resources in dynamic network settings.