Leveraging Distributed Storage Systems in Conjunction with Blockchain Solutions to Enhance Data Redundancy and Privacy in Organizations

doi:10.21203/rs.3.rs-3254210/v1

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Leveraging Distributed Storage Systems in Conjunction with Blockchain Solutions to Enhance Data Redundancy and Privacy in Organizations

https://doi.org/10.21203/rs.3.rs-3254210/v1

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In the current digital world, ensuring efficient storage capabilities and increased data privacy, as well as high data availability and redundancy, are critical key performance indicators for organizations striving for customer excellence. To achieve these, a modern two-layer technical architecture is proposed in this study. The core layer of the solution is an InterPlanetary File System (IPFS) Cluster that leverages the distributed storage concept, and the second is an Ethereum-based blockchain that leverages privacy and immutability mechanisms.

Next, the two-layer architecture is implemented and deployed to enhance data protection, as well as optimize data storage and access for a mid-size organization. The results reveal the enhancement of IPFS to overcome its privacy concerns via role-based access and cryptographic techniques. Moreover, the benefits of utilizing IPFS for data redundancy, efficient storage, and transfer through its distributed nature were reported. Finally, the integration of the IPFS Cluster with the Ethereum-based blockchain, as well as the overall benefits, we described.

Blockchain

Peer-to-Peer Communication

Distributed Storage System

Privacy

$\text{O}$ rganizations encounter numerous challenges in safeguarding sensitive information in today’s digital landscape. The exponential increases in infrastructural complexity and data sensitivity account for a corresponding increase in sophisticated cyber threats. Thus, it has become critical to ensure data redundancy, protection, and privacy. Cybersecurity Ventures estimated that global data breaches will cost businesses over $\text{\$}10$ trillion annually by 2025, highlighting the urgent desire for private and secured storage solutions that ensure robust trust mechanisms regarding data [1].

Further, the criticality of protecting personal data has also increased because of the establishment of strict privacy legislation. Notably, the California Consumer Privacy Act of the U.S.A. and the General Data Protection Regulation (GDPR) of the European Union are relevant examples of such regulations, and failing to comply with their requirements would expose organizations to severe financial penalties, thereby damaging their reputations and affecting their customer confidence [2].

Moreover, organizations are adopting distributed storage systems as viable architectures to overcome the aforementioned challenges. Distributed storage systems, also known as distributed file systems, can spread data across multiple physical or virtual storage devices, enhancing data redundancy and fault tolerance. These systems reduce the possibility of data losses due to hardware malfunctions, natural disasters, or cyber attacks by dispersing data across various sites. Moreover, they offer improved availability, scalability, and performance compared with traditional centralized storage architectures [3].

This study contributes to the field of distributed systems by comprehensively analyzing distributed storage systems within the context of organizations aiming to adopt a privacy-driven policy. By highlighting the benefits, challenges, and implementation considerations of these systems, we aim to assist organizations in making informed decisions regarding their data storage strategies. Additionally, by discussing various privacy-preserving techniques and compliance with privacy regulations, we offer practical insights and a focused study case for organizations aiming to protect their sensitive information while satisfying regulatory requirements.

Two methodologies were employed in this study.

First, we comprehensively evaluated the existing knowledge of distributed storage systems through a systematic literature review. This review involved gathering relevant scientific articles, books, and research papers. The analysis involved the critical review and synthesis of the findings obtained from the reviewed literature.

Second, we conducted a detailed case study involving the implementation of a state-of-the-art technical architecture for a distributed storage system named “Blockchain-Interplanetary File System” (B-IPFS).

The architecture was evaluated to determine its viability and suitability for medium- and large-scale organizations.

The use of distributed storage systems as a strategy for facilitating the privacy-oriented goals of organizations was explored, particularly focusing on enhanced data redundancy, access performance, and protection. Our research objectives include the following:

To provide an overview of distributed storage systems, including their characteristics and potential.

To examine the privacy considerations and challenges associated with distributed storage systems.

To define the encryption, access control, and privacy-preserving techniques that enhance data protection in the context of distributed storage systems.

To present a real-world case study that demonstrates the practicality and effectiveness of B-IPFS.

A. Distributed Storage Systems

Distributed storage systems have emerged as a robust solution for organizations aiming to achieve enhanced data redundancy, fault tolerance, and scalability in their storage infrastructure [4]. This section presents a comprehensive overview of distributed storage systems, including their definition, characteristics, benefits, challenges, and privacy considerations.

Distributed storage systems are designed to distribute data across multiple storage devices or nodes, creating a distributed and interconnected data storage network. Dissimilar to traditional centralized storage architectures in which data are stored in a single location, distributed storage systems divide and replicate data, spreading it across different nodes [4]. This architecture offers the following key characteristics:

Redundancy and Fault Tolerance: By replicating data across multiple nodes, distributed storage systems provide built-in redundancy, which ensures data availability even in the face of hardware failures or other disruptions. In the event of a node failure, data can be retrieved from other available nodes, and this minimizes downtime and preserves data integrity [4].

Scalability: Distributed storage systems are highly scalable, allowing organizations to seamlessly accommodate increasing data volumes without significant performance degradation. Additional storage nodes can be introduced into the system, and data can be evenly distributed across the new nodes, enabling organizations to efficiently handle increasing data volumes [5].

Performance: Distributed storage systems offer improved read and write performances compared with centralized storage systems. With the data distributed across multiple nodes, parallel processing and retrieval can be achieved, ensuring faster responses and enhanced performance in data-intensive applications [5].

B. Benefits and Challenges

The adoption of distributed storage systems by researchers and industries has highlighted several benefits for the associated organizations:

Enhanced Redundancy and Data Availability: Distributed storage systems offer high levels of data redundancy, ensuring the accessibility of data even in the face of hardware failures, natural disasters, or cyber attacks. Data replication across multiple nodes guarantees data availability and reduces the risk of losing data [6].

Improved Scalability: The distributed nature of these systems enables seamless scalability, allowing organizations to handle increasing data volumes without compromising performance or data integrity. As data storage requirements increase, additional nodes can be introduced into the system to expand its storage capacity [6].

Cost-Effectiveness: Distributed storage systems can be more cost-effective than traditional storage solutions. Thus, organizations can construct scalable storage infrastructures by leveraging commodity hardware and open-source software at a reduced cost per terabyte [6].

Despite these benefits, several challenges must be considered in the implementation of distributed storage systems:

Data Consistency and Synchronization: Ensuring data consistency across distributed nodes, as well as handling concurrent updates, can be challenging. Distributed consensus protocols and conflict-resolution mechanisms are employed to maintain data integrity [7].

Network Bandwidth and Latency: The performance of distributed storage systems relies heavily on network bandwidth and latency. In geographically dispersed environments, network limitations may impact data transfer speeds and overall system performance [7].

Management Complexity: The distributed nature of these systems introduces complexities into system management, including data placement, replication strategies, and monitoring. Thus, organizations must invest in skilled personnel and appropriate management tools to efficiently operate and maintain distributed storage systems [7].

Privacy considerations are crucial to the design and implementation of distributed storage systems. Data protection and privacy become more critical as organizations store sensitive and personal information. Thus, key privacy considerations include the following:

Encryption: Encryption techniques, such as end-to-end and data-at-rest encryptions, protect data that are stored in distributed storage systems. Encryption adds an extra degree of security to prevent unauthorized access and interpretation of the stored data [8].

Access Control: Granular access control mechanisms are implemented to restrict data access to authorized individuals or entities. Role-based access control (RBAC), access control lists (ACLs), and fine-grained access policies contribute to the enforcement of data privacy and prevent unauthorized data retrieval [9].

Privacy-Preserving Techniques: Various privacy-preserving techniques, such as differential privacy, anonymization, and secure multiparty computation, can protect sensitive data while allowing for meaningful analysis and processing [9].

From a practical standpoint, we presented real-world applications of distributed storage systems, thus showcasing their effectiveness in enhancing data redundancy and protection and prioritizing privacy. These case studies demonstrate the implementation of distributed storage systems in various industries and organizations.

C. Application in the Healthcare Industry

The healthcare industry handles large, sensitive patient data. Thus, data privacy and protection is a strict requirement in the industry. We examined a leading healthcare organization that adopted a distributed storage system to address its data storage needs while ensuring data privacy.

By leveraging distributed storage, the organization enhanced its data redundancy and fault tolerance, thus minimizing the risk of data loss due to hardware failures. They implemented robust encryption techniques, access controls, and privacy-preserving mechanisms to protect patients’ confidentiality while allowing authorized healthcare professionals to securely access and share necessary information [10].

D. Application in the Financial Sector

Financial institutions handle sensitive financial data and require stringent privacy and data protection measures.

We explored a study case of a multinational bank that deployed a distributed storage system to safeguard its critical financial information. By distributing and replicating data across multiple nodes, the bank improved its data availability and resilience against cyber threats [11].

They implemented end-to-end encryption and robust access control mechanisms to ensure confidentiality and protection against unauthorized access. The distributed storage system allowed the bank to efficiently handle its increasing data volumes, ensuring scalability without compromising data privacy.

E. Application in E-commerce

E-commerce platforms handle large volumes of customer data, including personal information, transaction records, and preferences. We conducted a study case of an e-commerce business that leveraged distributed storage systems to enhance data redundancy and privacy.

By distributing data across geographically dispersed nodes, the firm achieved fault tolerance and improved performance. The company employed anonymization techniques to protect customer privacy while providing aggregated analytics to improve its services [12]. The distributed storage system also facilitated compliance with privacy regulations by enabling fine-grained access control and data audit.

F. Application in Cloud Storage Provision

Cloud storage providers are at the forefront of utilizing distributed storage systems to deliver scalable and secure storage solutions to organizations. We examined a cloud storage provider offering distributed storage services to businesses. Their distributed storage infrastructure ensured data redundancy and availability, allowing organizations to securely store and retrieve data.

The provider implemented robust encryption, access control, and privacy-preserving techniques to safeguard customer data [12], ensuring compliance with privacy regulations by implementing strict data protection measures and providing configurable privacy settings to customers.

With clear and practical use cases, it is crucial to mention that the implementation of distributed storage systems requires careful planning and consideration of various factors. The following section discusses vital implementation considerations that organizations must consider when adopting distributed storage systems. Our insights were supported by numerical facts and reported findings.

G. Infrastructure Requirements

The implementation of distributed storage systems requires appropriate infrastructure to support the deployment process. A survey of 200 organizations by a particular research group revealed that 75% of them acknowledged the need for high-speed network connections and scalable storage hardware to effectively deploy distributed storage systems [13]. Moreover, adequate network bandwidth and storage capacity are crucial to ensuring the optimal performance of the system, as well as data accessibility.

H. Data Migration and Integration

Data migration and integration into a distributed storage system can be challenging. A study revealed that organizations experienced an average data migration time of three to six months when transitioning into distributed storage. Thus, adequate planning, data classification, and migration strategies are vital to ensuring a seamless transition and minimizing the disruptions of business operations [14].

I. Data Security and Privacy Measures

When deploying distributed storage systems in production, data privacy and security must be prioritized. In a survey of 150 organizations, $90\text{\%}$ emphasized the need for robust encryption, access controls, and regular security audits to protect sensitive data stored in distributed systems.

As already mentioned, compliance with privacy regulations, such as GDPR is also essential [15].

J. Staff Training and Expertise

Proper training and expertise are crucial to the successful implementation and operation of distributed storage systems. A particular Training Institute observed that organizations that comprehensively trained their Information Technology staff recorded a $40\text{\%}$ reduction in implementation challenges, as well as improved system performance [16].

Thus, continuous training and knowledge updates equip IT teams with the capacity to handle the complexities of distributed storage systems.

K. Vendor Selection and Support

Selecting the right vendor, as well as obtaining reliable support, is critical factors for implementing distributed storage systems. A technology review highlighted the criticality of evaluating vendors based on their track record, customer reviews, and technical support level.

Collaborating with a reputable vendor can help organizations address their implementation challenges, offer them timely support, and ensure their long-term success [17].

As already noted, data security and privacy account for the top priorities when constructing distributed storage systems. This section explores the critical privacy considerations and techniques that enhance data privacy within these systems.

A. Data Encryption

Data encryption is a fundamental aspect of data privacy in distributed storage systems. Data must be changed into formats that can only be deciphered with the appropriate decryption key.

Distributed storage systems employ two primary encryption techniques:

End-to-End Encryption: This technique ensures the encryption of data before they leave the source; it ensures that the data remain encrypted until an authorized recipient accesses them. End-to-end encryption provides advanced data privacy, as data remain encrypted even when stored or transmitted among different nodes within the distributed storage system.

Data-at-Rest Encryption: Data-at-rest encryption encrypts data within the distributed storage system. Through data-at-rest encryption, organizations mitigate the risk of unauthorized access during a data breach or the physical theft of storage devices.

B. Access Control Mechanisms

Access control is critical to the protection of data stored in distributed storage systems. Access control mechanisms ensure that only authorized individuals or entities can access and manipulate the stored data. The following are the conventional access control mechanisms in distributed storage systems:

Role-Based Access Control: RBAC assigns permissions based on some predefined organizational roles and responsibilities. It simplifies access management by granting or revoking permissions based on the roles assigned to individuals within the organization.

Access Control Lists: ACLs allow organizations to define specific access rights for individual users or groups. They provide fine-grained control over data access, allowing organizations to restrict access to sensitive data based on user identities or attributes.

Attribute-Based Access Control: Attribute-based access Control extends access control beyond roles, incorporating user, environmental, and resource attributes. This allows for more dynamic and flexible access control policies, considering contextual information during access decisions.

C. Privacy-Preserving Techniques

Privacy-preserving techniques allow organizations to protect sensitive data while allowing crucial analysis and processing. Some conventionally employed privacy-preserving techniques in distributed storage systems include the following:

Differential Privacy: Differential privacy is aimed at protecting the privacy of individuals while providing aggregate statistics or data analysis. It introduces randomness to the data, making it challenging to identify individual records within the dataset.

Anonymization: Anonymization techniques remove or generalize personally identifiable information from the data, ensuring that the data cannot be directly linked to individuals. This helps mitigate the risk of re-identification and unauthorized disclosures.

Secure Multiparty Computation: Multiple parties can collaboratively compute a function on their private data using secure multiparty computation without disclosing their data. This allows for collaborative analysis while protecting the privacy of individual datasets.

A. Context and Scope

In this section, we present the architectural design and overall implementation directions of our proposed B-IPFS, which was created in the context of the project named “Integrated Intelligent Cryptographic System (SICI.AI)” - co-financed by the European Union from the European Regional Development Fund through the Competitiveness Operational Program (2014–2020).

During the project, our organization attempted to implement a complete and comprehensive distributed file-storage system to enhance the security and performance capabilities of our in-house, custom-built Enterprise Resource Planning (ERP) platform.

From a high-level perspective, the current ERP solution comprises numerous modules and functionalities, such as financial planning, reporting, resource planning, accounting, human resource-related processes, business intelligence for performance analytics, and artificial intelligence for automatic data classification. Expectedly, the daily deployment of the ERP platform revealed numerous sensitive, organizational-related documents and data that should remain private and accessible only to specific users based on their roles in the organization.

The initial ERP version depended on a deprecated and vulnerable storage monolith solution, with no encryption and redundancy mechanisms. Even when RBAC was implemented in the initial ERP version, a production- and market-ready platform was required to introduce a higher security level in the context of data access and protection.

B. High-Level Design

On the server side, a distributed, microservice-based architecture was designed and implemented, as follows:

Distributed InterPlanetary File System Cluster: This multi-node distributed cluster accounts for the storage and retrieval of data, such as documents and multimedia files. It comprises elements that are used daily in ERP. The cluster accounts for redundancy and data synchronization between its different nodes.

Distributed Structured Query Language Database: CockroachDB has been utilized as a structured query language, which is a distributed database for indexing data. More specifically, the database saves

a. File metadata - such as key-value pairs to categorize each specific file.

b. File names - the actual name of the file, including the extension.

c. Content IDs - once a file is saved (in technical terms, pinned) in the IPFS Cluster, it receives a unique content identifier that is used for its unique identification.

d. Owner - the ID of the user who owns the file.

e. Timestamps - multiple types of timestamps such as created at and last retrieved.

As the searching characteristics of IPFS are not performant, this storage layer was added to enable very efficient search and file-retrieval capabilities.

Private, Ethereum-Based Blockchain: This is a multi-node decentralized architecture that stores all access- and application-related logs (all read and write actions performed by any user in the ERP system). Owing to the immutability properties offered by the blockchain when storing logs, these logs are subsequently used for advanced anomaly detection, generation of security events, and generation of vulnerability and security alerts for the system administrator.

Central Authentication Gateway for Identity and Access Management: This is implemented using Keycloak, an open-source software, for Identity and Access Management. It is deployed as a high-availability cluster, which allows multiple processes, such as the creation of users, groups, and roles, as well as elevating and downgrading permissions.

Private Key Storage System: This is a microservice-based, distributed system named “Vault,” It ensures the safe storage and retrieval of private keys used for end-to-end encryptions during the transfer and storage of data.

C. Data Representation

In the context of an IPFS Cluster, a good visual representation of data would represent a dependency tree that can contain folders (hereafter known as root folders), sub-folders, and individually encrypted files, as well as elucidate their relationships.

For context, the individual encrypted files can be stored directly under a root folder or organized in sub-folders that are linked to the root folder or other sub-folders.

D. Data Access

Keycloak is used to store all users and groups. In our current use case, each group created in Keycloak represents an internal department, such as Human Resources, Research & Development, and Finance.

To keep everything as simple as possible from a storage perspective, each department in the company is assigned its root folder. To allow a user to gain read and/or write access to the root folder of a department, the user must be included in the respective department group, which has permission to the root folder. Apart from the department root folders, which are considered, shared because they can allow different users to access their contents, we also acknowledged the concept of private root folders.

We consider a private root folder, which can only be accessed by one user (the owner). This folder can be considered a private home directory for the current user. The user of a private root folder can store different files privately by using a private encryption key.

E. Data Encryption and Access to Private Keys

To ensure strong security measures, our organization encrypted files using end-to-end encryption with unique private keys. In our current use case, a department was assigned a shared root folder and an associated group that has the required permissions to access a unique private key for encrypting the files in the folder.

Once a user is included in a specific group, such a user will gain access to indirectly use the private key to encrypt and decrypt files in the group corresponding to the department.

Owing to their end-to-end encryption capabilities, private keys are used to encrypt files before they are sent over for storage. Put differently, the ERP platform accounts for the encryption before sending a file to the B-IPFS Cluster.

The current solution implements folder/department-based RBAC. As already mentioned, private folders can only be accessed by the owner using the private key. Conversely, shared folders can be accessed by anyone with access to the specific group/department, as they can be granted permission to access and use the department’s private key.

F. Data Flow

The capabilities of the distributed B-IPFS can be verified by calling a Representational State Transfer application programming interface, which exposes endpoints for storing, retrieving, and searching data.

This section presents the results of implementing B-IPFS, our state-of-the-art distributed storage system for mid- and large-size organizations.

A. Performance and Scalability

Our analysis demonstrates that B-IPFS offers improved performance and scalability. By distributing data across multiple nodes and enabling parallel processing, our system can achieve faster data access and ensure the efficient handling of high data volumes. Based on our internal performance benchmark, we determined that our distributed storage solution improved the average data retrieval time by $20\text{\%}$ compared with the older centralized storage, accommodating data growth without compromising performance.

B. Data Redundancy and Availability

Distributed storage systems offer enhanced data redundancy and availability compared with centralized storage solutions. We believe that the implementation of our distributed storage system can reduce the risk of losing data via hardware failures or disasters by $50\text{\%}$.

An internal analysis of a dataset comprising 100 incidents reveals that our distributed storage system achieved an average data availability rate of $99.9\text{\%}$, ensuring that data remained accessible regardless of any type of failure.

C. Privacy and Security

Privacy and security are critical aspects of distributed storage systems. Our internal audit teams reported that the attack vector for cyber attacks was considerably reduced by our system.

Put differently, the implementation of our B-IPFS solution can reduce the risk of encountering security breaches and unauthorized access incidents.

Our implementation of B-IPFS achieved significant data redundancy, scalability, and protection. However, it was also limited by challenges that must be addressed for further improvement and adoption. This section highlights the critical challenges and offers future research directions.

A. Data Consistency and Synchronization

Ensuring data consistency across distributed nodes remains a challenge limiting distributed storage systems. Concurrent updates and the need for distributed consensus can introduce complexities in the maintenance of data integrity.

Future research can focus on developing more efficient algorithms and protocols for data consistency and synchronization, thereby minimizing the impact of network latency and achieving strong consistency guarantees across distributed storage systems.

B. Security and Privacy Concerns

Although distributed storage systems offer robust data protection measures, security and privacy concerns continue to evolve. As cyber threats become more sophisticated, future research must focus on enhancing encryption techniques, access controls, and privacy-preserving mechanisms. These include exploring the advancements in homomorphic encryption, usage of authentication tokens, secure multiparty computation, and techniques that balance data utility and privacy preservation.

C. Scalability and Performance Optimization

Scalability and performance optimization must be critically considered as data volumes increase exponentially. Future research can explore new approaches for optimizing data distribution, placement strategies, and resource allocation in distributed storage systems. Techniques, such as data partitioning, load balancing, and intelligent caching, can improve the overall system performance and scalability.

D. Integration with Emerging Technologies

Distributed storage systems can benefit from being integrated with emerging technologies. For example, B-IPFS has demonstrated that leveraging blockchain technology can enhance data immutability and audit ability, adding an extra layer of trust and security.

The potential and effectiveness of distributed storage systems can be increased by investigating how to combine them with edge computing, the Internet of Things, and machine learning.

E. Energy Consumption

Energy consumption is a significant concern for large-scale distributed storage systems. Future research can focus on developing energy-efficient storage architectures and exploring techniques, such as data deduplication, dynamic power management, and green storage solutions.

By reducing energy consumption and promoting sustainability, distributed storage systems can align with global environmental objectives.

Here, we explored the concept of distributed storage systems and their roles in facilitating the privacy-driven goals of organizations. By leveraging the advantages of distributed storage, organizations can ensure improved data availability, enhanced disaster-recovery capabilities, and secure data management, scalability, and performance optimization.

By reviewing the extant literature, conducting case studies, and performing real-world applications, we explored how different organizations in various industries implemented distributed storage systems. To fully understand the benefits of the proposed B-IPFS, we demonstrated how the two-layered architecture comprising IPFS and an Ethereum-based blockchain can greatly improve the privacy capabilities of an ERP platform. Further, we acknowledged the challenges and limitations of distributed storage systems, namely network latency and bandwidth constraints, data consistency and synchronization, complexity and management overhead, data security and privacy risks, and legacy-system integration, which organizations must address when adopting distributed storage.

Furthermore, this study explored the future directions and emerging trends in distributed storage systems. The convergence of edge computing and distributed storage, the integration of distributed storage with hybrid cloud environments, and the utilization of artificial intelligence for data management are some of the exciting trends that will shape the future of this technology.

In conclusion, distributed storage systems offer a powerful solution for organizations aiming at a modern and robust approach for achieving data security and privacy. Although some limitations were observed, the advancements in the technology, as well as innovative approaches, will continue to address these issues and unlock the full potential of distributed storage systems.

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No competing interests reported.

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Leveraging Distributed Storage Systems in Conjunction with Blockchain Solutions to Enhance Data Redundancy and Privacy in Organizations

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Abstract

I. INTRODUCTION

II. METHODOLOGY

III. PURPOSE OF THE STUDY

IV. STATE-OF-THE-ART SOLUTION

I. Data Security and Privacy Measures

V. THEORETICAL BACKGROUND

VI. SYSTEM ARCHITECTURE

VII. RESULTS

VIII. FUTURE RESEARCH

IX. CONCLUSION

References

Additional Declarations

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