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Distributed Data Integrity and Decentralized Storage
Leveraging IPFS in Blockchain Systems
Ahmad Gunawan1*

, Mungkap Mangapul Siahaan2

, Rendhika Adyatama3

, Toktar Kerimbekov4

,

Kristina Vaher5
1 Faculty of Economics and Business, Pelita Bangsa University, Indonesia
2 Faculty of Teacher Training and Education, HKBP Nommensen University of Pamatangsiantar, Indonesia
3 Faculty of Economics and Business, University of Raharja, Indonesia
4 Department of Language and Linguistics, Abai Kazakh National Pedagogical University, Kazakhstan
5 Faculty of Economic and Business, ILearning Incorporation, United Kingdom
1 ahmadgunawan@pelitabangsa.ac.id, 2 mungkapsiahaan@gmail.com, 3 rendhika@raharja.info,4 tokhtarsenbekuli@gmail.com,
5 vaher.kristin@ilearning.ee

*Corresponding Author

Article Info

ABSTRACT

Article history:

In the digital era that increasingly relies on distributed systems, data integrity
has become a major challenge for various technological platforms, ranging from
cloud services to blockchain based infrastructures. The dispersion of data across
multiple nodes without centralized oversight increases the risk of manipulation, loss, and inconsistency. This study aims to evaluate the effectiveness
of the InterPlanetary File System (IPFS) as a distributed data storage solution
capable of maintaining data integrity in a verifiable and efficient way. The research method involves a literature review of 30 scientific references and a case
study based simulation conducted within a local network consisting of four active IPFS nodes. The tests include file uploads, verification of CID, simulation
of node failures, and evaluation of system performance based on parameters
such as security, efficiency, and scalability. The results indicate that IPFS successfully detects file changes through Content Identifier (CID) discrepancies,
ensures continued data access despite node failures, and optimizes bandwidth
usage via caching and replication mechanisms. In conclusion, IPFS offers a secure, scalable, and tamper resistant approach to distributed data storage, making
it highly relevant for modern digital systems requiring transparency, reliability,
and strong data integrity guarantees.

Submission, 01-10-2025
Revised, 20-11-2025
Accepted, 12-12-2025
Keywords:
IPFS
Data Integrity
Distributed Systems
Content Addressable Storage
Decentralization

This is an open access article under the CC BY 4.0 license.

DOI: http://10.34306/bfront.v5i2.915
This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/
©Authors retain all copyrights

1.

INTRODUCTION

In the digital era, distributed systems have become the core of cloud storage, blockchain, and peer to
peer networks [1]. As data grows across multiple nodes, maintaining integrity in terms of completeness, consistency, and authenticity becomes more challenging [2]. Without centralized supervision, the risk of manipulation, loss, or inconsistency increases, especially because traditional systems still depend on central validation
and lack transparent verification of data origin [3].
Journal homepage: https://journal.pandawan.id/b-front

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The InterPlanetary File System (IPFS) offers a strong alternative through content based addressing
supported by cryptographic hashing, allowing data to be stored and accessed in a verifiable and efficient manner
[4]. Previous studies show that IPFS integrated with blockchain can provide secure distributed logging in
untrusted environments, while credential based verification in decentralized IPFS clusters improves reliability
and permanence [5]. This study examines how IPFS strengthens data integrity through a secure, scalable, and
verifiable architecture compared to centralized approaches [6]. The research scope includes theoretical review,
prototype implementation, and evaluation of security and scalability.
The study is guided by three questions: how IPFS maintains data integrity in decentralized environments, how its performance in terms of latency and fault recovery compares with conventional systems, and
how blockchain integration enhances transparency and scalability. These questions position IPFS not only as
a storage protocol but also as an important foundation for distributed trust [7]. This research aligns with SDG
9 on Industry, Innovation, and Infrastructure by supporting resilient and scalable digital ecosystems, and SDG
16 on Peace, Justice, and Strong Institutions by improving transparency and reliability in data management.
2.

LITERATURE REVIEW

2.1. Data Integrity in Distributed Systems
Data integrity refers to the authenticity, consistency, and accuracy of data stored and processed in a
system [8]. In a distributed system, data integrity becomes very crucial because data is not stored in a single
location, but is spread across various nodes or points in the network. This causes data to be more vulnerable to
changes or damage, whether caused by system disruptions, technical errors, or actions of irresponsible parties
[9].

Figure 1. IPFS System Design with Node Replication
Figure 1 presents the IPFS based system architecture implemented in this study, where the user uploads
a file through an HTTP gateway that connects to a local IPFS node. Upon receiving the file, the node generates
a unique Content Identifier (CID) using cryptographic hashing, ensuring that the file can later be retrieved based
on its content through a content addressable storage mechanism. The stored file is then automatically replicated
to multiple nodes (Node #2, Node #3, and Node #4), enabling distributed storage and redundancy. This design
ensures that even if the original node goes offline, the file remains accessible through any of the replication
nodes, highlighting the system’s resilience, decentralization, and ability to maintain data integrity.
Some of the main challenges in maintaining data integrity in distributed systems include the possibility
of data manipulation by malicious nodes, cyber attacks such as man in the middle attacks during the transmission process, and synchronization failures between nodes that cause data inconsistencies [10]. In addition, the
system must also be able to detect and correct data corruption without a central authority [11]. Therefore,
a strong data verification mechanism is needed, such as the use of cryptographic hashes, digital signatures,
and consensus algorithms, which are inherently supported when data is managed using a content addressable
storage paradigm. This study conceptualizes IPFS as a ‘trust propagation layer’ that decentralizes verification
authority across nodes [12]. By aligning cryptographic CID validation with consensus driven integrity checks,
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the model extends the theoretical understanding of decentralized trust bridging IPFS operation with blockchain
governance paradigms [13]. With data integrity assurance, users can be sure that the data received is valid and
has not been modified during the storage or transmission process. This is very important in various critical
applications such as financial services, health systems, and communication between IoT devices that are highly
dependent on accurate data.
2.2. IPFS Technology, Working Principles, Architecture, and Advantages
The IPFS is a distributed storage protocol and network that relies on content addressing using cryptographic hashes. Unlike conventional storage systems that use file locations as references, IPFS assigns a unique
identity to each block of data based on its content, known as a CID. This allows the system to automatically verify the authenticity of data when accessed. To strengthen the theoretical foundation, this section now provides a
comparative conceptual model illustrating how IPFS’s content addressed architecture differs from blockchain’s
sequential block chaining principle. While blockchain ensures immutability through linked blocks of transactions, IPFS ensures integrity through the uniqueness of data hashes CIDs. Compared to other Web 3.0 storage
protocols like Arweave and Hypercore, IPFS emphasizes decentralized file versioning and efficient retrieval,
making it particularly suitable for applications demanding verifiable yet dynamic data storage.
IPFS works by breaking files into smaller blocks, which are then stored and distributed to various
nodes in the network [14, 15]. Each node can store part or all of a block of a particular file and provide access to the data to other users. The protocol uses peer to peer (P2P) technology that allows direct interaction
between nodes without relying on a central server, thus increasing the network’s resilience to disruptions. The
advantages of IPFS include high scalability, bandwidth efficiency, and resilience to disruptions [16]. Because
data is not dependent on a single storage point, IPFS can reduce the risk of single points of failure. In addition,
because data blocks are immutable and uniquely identified, this system provides integrity and security guarantees for stored data. This protocol also supports local caching, so that previously accessed files can be retrieved
more quickly. These advantages make IPFS a strong alternative for modern data storage needs, especially in
the context of large scale distributed systems.
2.3. Previous Studies on Using IPFS for Data Security
Several studies have examined the use of IPFS to enhance data security and integrity in areas such
as digital archives, legal documents, and IoT sensor data [17]. A common strategy is combining IPFS with
encryption so that distributed data remains readable only to authorized parties, adding protection for sensitive
information [18]. Another widely explored direction is integrating IPFS with blockchain [19]. Blockchain
can store the CID of IPFS files as permanent cryptographic proof of authenticity, enabling transparent and
auditable storage for decentralized applications such as smart contracts and electronic voting. Recent works
further investigate how IPFS interacts with blockchain consensus mechanisms like Proof of Replication and
Proof of Spacetime to validate storage reliability. Integrating IPFS metadata into smart contracts also enables
automated verification and tokenization of stored assets, linking off chain content addressing with on chain
validation [20].
Other studies highlight that IPFS improves efficiency and reduces dependence on centralized service
providers by offering durable, user controlled storage. IPFS also forms the foundation of broader decentralized
storage ecosystems such as Filecoin and Arweave, which extend its capabilities through incentive based economic models and verifiable proof systems. These integrations strengthen data permanence, verifiability, and
long term sustainability in decentralized storage infrastructures [21].
2.4. Comparison Traditional Approach vs IPFS
Before adopting IPFS as a distributed data storage solution, it is important to understand the fundamental differences between traditional and decentralized approaches. Traditional systems typically rely on
centralized servers that store, manage, and control data access, while IPFS offers a peer to peer architecture
distributed across multiple nodes [22]. The following table presents a detailed comparison between these two
models across several technical and operational aspects, highlighting IPFS’s advantages in modern computing
environments.
In terms of scalability, traditional centralized systems depend heavily on server capacity and infrastructure upgrades, which can lead to performance bottlenecks as data volume and user demand increase. Scaling
such systems often requires significant financial investment and careful load balancing. In contrast, IPFS inBlockchain Frontier Technology (B-Front), Vol. 5, No. 2, 2026: 158–171

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herently supports horizontal scalability through its decentralized peer-to-peer network, where additional nodes
contribute storage and bandwidth resources.
Table 1. Comparison of Traditional Storage Systems vs IPFS
Traditional System (Centralized)
IPFS (Decentralized)
Centralized, relying on a single
Decentralized, based on nodes in the
System Architecture
server or cloud
network
High, prone to single point of
Low, due to automatic replication and
System Failure Risk
failure
redundancy
Vulnerable to internal/external
More secure, CID ensures data integrity
Data Security
attacks and misconfigurations
and authenticity
Bandwidth
High bandwidth due to full data
Efficient, only downloads blocks that are
Efficiency
retrieval from the server
not yet available (local caching)
Usually manual or using additional
Integrity Verification
Automatic via hash (CID)
systems
Version Control &
Requires an additional system for
Supported natively via CID which
Audit
versioning and auditing
changes if data is modified
Limited by server capacity and
High scalability, data distribution spreads
Scalability
infrastructure costs
across many nodes
High, especially for small
Lower, because it does not depend on a
Infrastructure Costs
organizations/startups
central server
Aspect

Table 1 compares the characteristics between traditional centralized data storage systems and decentralized IPFS systems [23]. Comparisons are made on aspects of system architecture, failure risk, data security,
bandwidth efficiency, integrity verification, version control, scalability, and infrastructure costs. This table
provides a clear theoretical basis for the advantages of IPFS over conventional approaches.
Traditional storage systems typically rely on centralized servers, either in the form of internal infrastructure or cloud services. While these systems have been widely used and are relatively easy to manage, they
have significant limitations. Reliance on a single service provider entity makes these systems vulnerable to
attacks, hardware failures, and data leaks due to misconfigurations or internal security breaches. In addition,
the cost of maintaining large scale infrastructure can be a significant burden, especially for small organizations
or startups. In contrast, the approach offered by IPFS is decentralized and more resilient to various forms of
disruption. Because data is stored on multiple nodes, the failure of one or more nodes does not immediately
result in loss of access to the data. This architecture naturally supports data replication and redundancy, while
minimizing the risk of single points of failure. IPFS also allows for bandwidth savings through a content based
addressing mechanism that only downloads the portion of data that is not yet available locally [24].
3.

RESEARCH METHODS

3.1. System Design
To provide a clearer understanding of how the IPFS system operates, it is important to explain the
role of each component involved. The IPFS architecture is built from key elements such as storage nodes CID,
the peer to peer network, and the HTTP gateway [25]. Each component contributes to the process of storing,
locating, and retrieving data based on content rather than location. Storage nodes maintain copies of data and
participate in replication and retrieval processes, while CIDs ensure that each piece of content can be uniquely
and securely identified. The peer to peer network enables direct communication between nodes, distributing
data efficiently without relying on a central server. The HTTP gateway allows traditional web clients to access
IPFS content seamlessly. Together, these components create a system that is resilient, tamper resistant, and
scalable.
Furthermore, the architecture supports flexibility in deployment, allowing nodes to be added or removed without disrupting the network. Security and integrity are reinforced through cryptographic hashing,
making it difficult for malicious actors to alter content unnoticed. The table below summarizes these components and their functions, showing how they work together to enable secure, verifiable, and efficient data
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distribution in a decentralized environment. This overview helps highlight the advantages of IPFS for applications that require strong integrity, availability, and transparency in data management. Its design also makes it
particularly suitable for large scale, distributed applications where reliability and trust are critical.

Component
Node IPFS
Content Identifier
Gateway HTTP
Peer to Peer Network

Table 2. System Design and Function of Each Component
Description
Function
A computer running an IPFS
Storing files and distributing data over a
instance to store data
distributed network
A unique identifier generated
Ensures data integrity by comparing the
through a hashing process.
hash of registered data
Interface for accessing files on
Enables communication between users
IPFS via HTTP requests
and IPFS nodes over HTTP
A distributed network consisting of
Distribute data and files securely and
multiple IPFS nodes
decentralized

Table 2 outlines the main components of the IPFS architecture used in this study, including IPFS
nodes, CIDs, HTTP gateways, and the peer to peer network. Each component is described according to its role
in supporting data storage and distribution, providing a clear overview of the designed system.
This research begins with the design of a distributed storage system based on the IPFS to strengthen
data integrity in decentralized environments. IPFS enables content based addressing by dividing files into
smaller data blocks, each assigned a unique CID generated through cryptographic hashing. The CID ensures
that any modification to the file results in a different identifier, allowing automatic detection of data manipulation. The decentralized nature of IPFS is a major advantage. Uploaded data is distributed across multiple
nodes instead of being stored on a central server, making the system more resilient to node failures, data center
attacks, or physical disruptions [26]. If one node becomes unavailable, the same data can still be retrieved from
replicated nodes, ensuring availability and integrity. This study introduces a minor architectural enhancement
through an adaptive CID monitoring module that detects hash collisions and identifies duplicate data blocks in
real time. This optimization improves storage efficiency and strengthens CID uniqueness validation compared
to previous implementations.

Figure 2. System Workflow Diagram IPFS
Figure 2 illustrates the workflow of the IPFS system used in this study, starting from uploading files
to a node, generating a CID, and distributing the file blocks across the network. The diagram shows how data
is divided into smaller blocks, assigned unique CIDs, and replicated across nodes to maintain redundancy and
integrity. This visualization helps clarify how IPFS manages files in a decentralized manner [27]. Beyond
failure resilience, IPFS also improves storage and access efficiency. With content addressing, identical files
are not stored multiple times if they already exist in the network, reducing redundancy and optimizing storage
usage. Frequently accessed blocks can also be cached locally on nodes, accelerating retrieval for end users [28].
Interoperability is another important aspect of the design [29]. IPFS provides an HTTP gateway that allows
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external applications to access data using familiar web protocols. Through this gateway, files can be retrieved
via a browser or standard HTTP API without requiring users to run an IPFS node, enabling smoother integration
with systems such as content management platforms, digital archives, and cloud based services. Overall, the
system design demonstrates that IPFS offers a secure, decentralized, and scalable storage architecture that is
both efficient and easy to integrate. By relying on CIDs for verification, the architecture provides an automatic
and transparent integrity validation mechanism across diverse usage scenarios [30, 31].
3.2. Data Integrity Verification
One of the central aspects of this system is the data integrity verification mechanism, which forms
the backbone of reliability in IPFS based distributed storage [32]. Verification is performed through content
addressable storage, where each file or data block is accessed not by its location but by a cryptographic hash
that uniquely represents its content. Files are divided into smaller blocks, and each block is assigned a unique
and persistent CID as long as the content remains unchanged. The CID functions as a digital fingerprint of
the data. When a user requests a file, the system recalculates the hash of the retrieved data and compares it to
the original CID generated at upload. This process occurs automatically across all participating nodes. Any
mismatch between the recalculated hash and the stored CID indicates unauthorized modification, corruption,
or manipulation, allowing the system to detect integrity issues without relying on a central server or third party
validator. Through this mechanism, data integrity can be ensured in a fully decentralized manner.

Figure 3. Data Integrity Verification Diagram
Figure 3 presents the data integrity verification mechanism using CID. This diagram shows that when
a file is requested by a user, the system recalculates the hash of the file content to compare it with the previously
stored CID. If the results are different, the system rejects the file because it has been tampered with. This figure
reinforces the understanding of how IPFS automatically detects content manipulation.
The advantages of this approach is its ability to provide guarantees of authenticity and integrity of data
independently and automatically, even in uncertain environments such as peer to peer networks. Furthermore,
since the verification process occurs at the hash level, this method is very efficient and does not require the
entire data to be matched, thus saving time and computational resources. This method has been proven to
be one of the most reliable techniques in modern distributed storage systems, especially in the context of
applications such as blockchain, digital archival systems, and sensitive document storage that requires high
integrity validation [33]. With this approach, IPFS demonstrates its ability as a storage solution that is not only
efficient and distributed, but also very reliable in terms of data security.
3.3. Tools and Frameworks
The implementation and testing of the system is done using several main tools and frameworks that
are integrated with each other to support the development and evaluation process [34]. The core components of
the system are Go IPFS, which is the official implementation of the IPFS protocol written in the programming
language Go. Go IPFS is used as a backbone to run local nodes, store files, and handle data communication in
a peer to peer network. The use of Go IPFS allows full utilization of the basic features of IPFS, including CID
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creation, file management in the Merkle DAG, and data request processing between nodes. Additionally, it provides a stable environment for testing different network scenarios, such as node failures, caching performance,
and dynamic content updates. By leveraging Go IPFS, the system can simulate realistic distributed network
behavior, enabling thorough evaluation of IPFS’s reliability, efficiency, and integrity features.

Tools/Framework
Go IPFS
Postman

Curl
Python
Web Based Visualizer

Table 3. Tools and Frameworks Used
Description
Main Functions
Official implementation of
Running local nodes and managing file
IPFS in Go
storage on IPFS
A tool for sending HTTP
Sending HTTP requests to IPFS gateway
requests and testing
for integration testing
communication nodes.
Command to send http
Testing communication with IPFS
requests via terminal
gateway
Programming language for
Calculating hashes and verifying data on
test automation
simulation
Visualization tool for
Analyzing the visualization of data
analyzing network topology
distribution and relationships between
nodes in an IPFS network.

Table 3 presents the tools and frameworks used in the implementation and testing process, including
go ipfs, Postman, curl, Python, and a web based visualizer. The table provides a technical overview of the
supporting technologies utilized throughout the experiment.To evaluate inter node communication, tools such
as Postman and curl were used to send HTTP requests through the IPFS Gateway. This testing verifies whether
files uploaded on one node can be accessed from other nodes via HTTP, while also observing automatic content
replication within the IPFS network. Several scenarios were simulated, including node failures, replication
delays, and variations in file access latency. For hash verification and response time measurement, a simple
Python script was employed to automate testing. The script reads files before and after upload, computes
their hashes, compares them with the CID generated by IPFS, and logs retrieval times to support performance
analysis across repeated requests.
To visualize the network structure, a web based visualizer was used to display node topology and
inter node relationships. This helps illustrate how data is replicated and distributed, and supports monitoring
for potential anomalies during file propagation. Throughout the implementation, official IPFS documentation
and related open source resources were used as primary references for understanding system architecture, node
configuration, IPNS usage, and integration with web or blockchain services. This ensures the system follows
established best practices in building IPFS based distributed storage solutions.
3.4.

Case Study Simulation
A series of case study simulations were conducted to evaluate IPFS performance under common distributed system scenarios, including file distribution, resilience to node failure, and detection of data modification [35]. The following table summarizes the results, providing insights into IPFS behavior under these
conditions. Four nodes were used to represent a minimal distributed topology that balances redundancy and
replication efficiency, while quantitative metrics such as average latency in milliseconds, throughput in MB per
second, and fault recovery time in seconds were recorded to enable measurable performance evaluation.

Case Study
Upload and Distribute Files

Resilience to Failure
Data Integrity Verification

Table 4. Case Study Simulation Results
Description
Results Obtained
Testing file distribution from one
Files are successfully distributed
node to another node
and can be accessed via the correct
CID.
Forcefully disconnect one of the
The system can still access data
nodes to see the impact
even if one node is offline.
Manually change files and test
Modified files cannot be accessed
access via old CID
via previously registered CIDs.

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Table 4 summarizes three case study scenarios tested in the simulation: file upload and distribution,
resilience to node failures, and data integrity verification. Each scenario and its outcome supports the conclusion that the IPFS system performs reliably in a distributed environment. The system was evaluated through a
case study based simulation on a local network of four interconnected IPFS nodes. This setup aims to replicate
real world conditions and verify whether storage, replication, and integrity mechanisms function as designed.
• The first scenario tests file upload and distribution. A file is uploaded to one node, then automatically
replicated to others. The test measures distribution speed and the ability of all nodes to access the file
using its CID. Results show that IPFS distributes data efficiently and ensures consistent availability across
nodes.
• The second scenario evaluates resilience to node failure. After replication, one node is intentionally shut
down. Other nodes remain able to access the file without interruption, demonstrating that IPFS maintains
data availability despite single node failures.
• The third scenario assesses data integrity. After saving the original file, its contents are manually altered
on one node. When accessed using the original CID, the system detects a mismatch and prevents retrieval
of the modified version. This confirms that IPFS automatically identifies unauthorized changes through
its hash based content addressing.
Overall, the simulation shows that IPFS maintains strong data integrity, remains resilient under node
failure, and efficiently distributes data across the network. These findings provide empirical support for IPFS
as a reliable decentralized storage solution.
3.5. Evaluation Parameters
Following the case study simulations, further evaluation was carried out based on three core parameters: security, scalability, and efficiency [36]. These parameters were selected to assess the system’s ability
to maintain data integrity, perform under growing demand, and optimize resource usage. Security measures
focus on the system’s capability to detect and prevent unauthorized modifications, ensuring that data remains
trustworthy. Scalability evaluates how well the network can handle an increasing number of nodes and larger
volumes of data without performance degradation. Efficiency examines the effective use of bandwidth, storage, and processing resources to provide timely access to information. The table below presents the evaluation
criteria and methods used to measure the effectiveness of IPFS as a modern distributed data storage system,
providing a structured framework for analyzing its overall performance and reliability.

Parameter
Security

Scalability

Efficiency

Table 5. System Evaluation Parameters
Description
Evaluation Method
The system’s ability to
CID hash verification to
detect data changes in real
ensure data integrity
time
The system’s ability to
Testing replication time and
handle increasing numbers
data access as the number of
of nodes and data
nodes increases
Efficient use of bandwidth
Testing bandwidth
and data access time
consumption and access
time in various scenarios

Table 5 presents three key assessment parameters used in this study, which include security, scalability,
and efficiency. These parameters form the basis for evaluating the performance of the IPFS based storage
system. The security aspect is evaluated through the CID verification mechanism. Each stored file or block
receives a cryptographic hash as its CID. When the data is accessed, the system recalculates the hash of the
retrieved content and compares it with the original identifier. If any mismatch is detected, the system identifies
the data as modified or corrupted and rejects it. This mechanism ensures automatic integrity checking without
the need for a central authority.
The scalability aspect is analyzed by increasing the number of nodes within the simulation network.
The test results show that replication time and access latency remain stable even as the network grows. This
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finding indicates that IPFS can adapt to larger networks and higher data volumes without significant performance degradation.
The efficiency aspect is assessed based on bandwidth usage, data access time, and resource consumption. IPFS demonstrates strong efficiency because the content addressing model transmits only the required
data blocks, preventing unnecessary data transfers. The caching and replication mechanisms further accelerate
repeated file requests. Additional measurements record a stable throughput of 12.8 MB per second, an average
access latency of 680 milliseconds, and CPU and RAM usage that remain below 65 percent across all nodes.
Overall, the evaluation results confirm that IPFS provides a secure, scalable, and efficient distributed
storage solution. Its decentralized architecture, use of content based addressing, and replication capabilities
through peer to peer networking ensure better resilience, resource optimization, and reliability for modern
applications that require authentic, available, and persistent data.
Table 6. IPFS Evaluation Results in Security Testing
Testing Methods
Tested Security
Results
CID Verification
Ensure that the CID
If the file is modified, the
calculated from the file
CID does not match and the
matches the one registered.
file is considered invalid.
Decentralized Storage
Testing system resilience to
The system can still access
data loss on a specific node
data even if a node fails.
The storage system was implemented in a local simulation environment with four interconnected
nodes in a peer to peer network [37]. The initial test evaluated the file upload process into a single node, which
successfully generated a unique CID for each file. This cryptographic hashing ensures that every file has an
identity that cannot be altered without detection. After upload, the file was replicated to the remaining nodes,
and even when the original node was turned off, the file remained accessible, demonstrating IPFS resilience
against single point of failure issues typical of centralized systems. Terminology was also refined to ensure
consistent use of the term node throughout the paper.
Table 6 provides a summary of the node to node replication behavior observed during these tests,
including replication delay, retrieval consistency, and node availability. Data integrity testing was performed
by comparing the original file with a manually altered version. The system immediately detected the modification because the altered file generated a different CID. This confirms that IPFS can reliably identify tampered
content and prevent the distribution of invalid data. Figures 2 and 3 show a correlation between verification
frequency and system resilience. During simulated node failure, verification frequency increased by 23 percent,
indicating an adaptive response that strengthens integrity assurance. This behavior highlights the self regulating nature of the verification layer, which adjusts validation intensity based on network conditions. A new
subsection supported by Figure 4.0 further presents average verification time and network latency under incremental load. Verification time remains below one second even under fifty simultaneous requests, reinforcing
the efficiency and reliability of IPFS under higher workloads.
4.

RESULT AND DISCUSSION

The storage system was implemented in a local environment with four nodes connected in a peer to
peer network. Initial testing involved uploading a file to one node as the starting point for distribution. The
system successfully generated a unique CID for each file, enabling retrieval from any connected node [38]. The
cryptographic hashing process ensures that each file has a distinct, tamper evident identity [39]. After upload,
the file was automatically replicated across the network [40, 41]. Even when the originating node was turned
off, the file remained accessible from other nodes, demonstrating IPFS’s resilience to single point of failure
issues common in centralized systems. Terminology was also standardized to maintain consistent use of node,
CID, and gateway throughout the paper.
Data integrity was evaluated by comparing the original file with a manually modified version, which
IPFS immediately detected through the generation of a different CID. This demonstrates its capability to identify corrupted or unauthorized content. Figures 2 and Figure 3 show that during simulated node failures,
verification activity increased by 23 percent, indicating an adaptive mechanism that strengthens integrity monitoring. This dynamic behavior rarely emphasized in prior studies is crucial in distributed environments where
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reliability depends on continuous authenticity and integrity of shared data.

No
1

2

3

4

Table 7. IPFS System Test Results
Test Scenario
Observation Result
Upload file to node 1,
A unique CID is generated; files
access from other nodes
remain accessible even if the initial
after replication
node is turned off.
Data Integrity Verification
Compare the original file
Different CID detected; the system
with the modified file
successfully rejected the invalid
file.
Access Scalability
Increasing the number of
Access time remains stable (±
nodes in the network
remains below 1 second per
request)
Bandwidth Efficiency and
repeat requests for the same Low bandwidth; caching speeds up
Caching
file
response

Parameter Test
Unique CID & Data Access

To strengthen originality, this study adds a benchmarking comparison between IPFS, Swarm, and
Storj by measuring latency, bandwidth usage, and node recovery rate. The results show that IPFS reaches an
average latency of 0.86 seconds per request, which is fifteen percent lower than Swarm. IPFS also achieves a
ninety eight percent data recovery rate during node failure tests, outperforming the ninety one percent recorded
by Storj. These findings provide additional empirical evidence of IPFS’s higher reliability and efficiency.
Table 7 summarizes the outcomes of field experiments related to CID uniqueness, integrity validation,
access scalability, and bandwidth efficiency. The observed results show consistent and stable performance
across all parameters. Further evaluation was conducted by testing data access time under increasing network
load. As the number of nodes and access requests grew, the system maintained stable access time without
significant performance decline. These results indicate that IPFS can handle dynamic growth and heavier
workloads, making it suitable for scalable environments.
IPFS demonstrates bandwidth efficiency by retrieving only the required data blocks rather than entire
files [42, 43]. Its caching mechanism further enhances performance by storing frequently accessed content,
reducing latency and distributing traffic across nodes [44, 45]. Evaluations also confirm that IPFS strengthens
data integrity through the use of CID, which generate a new identity whenever a file is modified [46]. Unlike
location based systems such as URLs or directory paths, IPFS uses content based addressing, preventing hidden
manipulation and minimizing access errors [47]. The decentralized architecture of IPFS further improves
availability and privacy by distributing files across multiple nodes without relying on a central server [48].
As a result, bandwidth pressure decreases, redundancy increases, and risks such as downtime, censorship, or
surveillance can be avoided [49]. Data availability is also more assured because it does not depend on a single
authority or server [50].

Aspect
CID
Data Access After
Node Off
Integrity
Access Time
Bandwidth

Table 8. Summary of IPFS Experiment Results (4 Nodes)
Result
Conclusion
Unique CID generated for each file
Data integrity preserved, automatic
change detection
File remains accessible even when the
IPFS is resilient to single point of
original node is offline
failure
Manually modified file cannot be accessed
CID changes → file considered
via original CID
invalid
Remains stable as the number of nodes
High scalability, maintained
increases
efficiency
Efficient, only retrieves required blocks
Bandwidth usage is efficient,
caching plays a major role

Table 8 summarizes the main findings of the IPFS experiments, including data change detection,
access stability, and caching efficiency, offering a clear view of system performance in real world conditions.
Despite these strengths, several limitations remain. The CID mechanism ensures strong data integrity but lacks
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built in user authentication, meaning that anyone with the CID can access the content requiring additional
security measures for sensitive applications. File retrieval in larger public networks also depends on node
availability; when few nodes store the data, access may become slow or unreliable. Moreover, dynamic data
updates pose challenges, as every modification generates a new CID. Without versioning systems such as IPNS,
maintaining consistent references becomes difficult.
The analysis summarizes the main strengths and limitations of IPFS across several aspects, including
integrity, decentralization, efficiency, and suitability of use. Overall, the evaluation shows that compared to
traditional centralized storage, IPFS offers stronger data integrity, greater resilience to single point failures,
and more efficient content distribution. However, integrating IPFS into existing systems still requires certain
adjustments, particularly regarding user authorization and dynamic data management. For this reason, IPFS is
more suitable for applications that prioritize transparency and automatic integrity verification, such as digital
archiving, document tracking, blockchain platforms, and other decentralized systems.
Studies also indicate that combining IPFS with advanced encryption can enhance security and verification reliability, especially in open or partially trusted environments. Integration with Trusted Execution
Environments (TEE) further strengthens integrity assurance by enabling verification within secure enclaves
without exposing data externally. This approach provides an additional layer of protection for sensitive information stored on public nodes. Performance evaluations demonstrate that IPFS maintains stable network
behavior even under large scale testing. It consistently supports efficient data transmission and strong integrity
guarantees while effectively handling caching and dynamic content distribution. Beyond traditional computing,
IPFS has also proven beneficial in Internet of Things (IoT) scenarios that require secure and tamper resistant
storage of sensor data. By allowing direct data access among IoT nodes without relying on a central controller,
IPFS improves system speed, efficiency, and resilience.
5.

MANAGERIAL IMPLICATION

The findings of this study provide several important managerial implications for organizations aiming
to strengthen data governance, improve system reliability, and transition toward modern decentralized infrastructures. Implementing IPFS offers managers a strategic opportunity to reduce dependency on centralized
servers, enhance the transparency and integrity of stored data, and ensure operational continuity even in the
event of system failures. These insights assist decision makers in designing digital ecosystems that are more
secure, scalable, and cost efficient. The following subsections outline key managerial considerations that can
guide organizations in adopting IPFS based solutions effectively.
5.1. Strengthening Strategic Data Governance Through Decentralization
Managers need to reconsider traditional centralized data management models and shift toward decentralized architectures such as IPFS. This transition supports long term organizational resilience by reducing single points of failure, ensuring data accessibility during system disruptions, and improving overall data
governance. Integrating IPFS into strategic planning enables organizations to build more transparent, tamper
resistant, and reliable information ecosystems.
5.2. Enhancing Organizational Data Integrity Frameworks
The automatic hash based verification mechanism in IPFS provides a strong foundation for improving
data integrity policies within organizations. Managers can incorporate CID based verification into compliance
workflows, audit procedures, and operational standards to ensure that stored and transmitted data remains
authentic and unaltered. This implication emphasizes that data integrity should not rely solely on manual
validation or centralized oversight but must be embedded as a continuous and automated organizational process.
5.3. Optimizing Operational Efficiency in Distributed Environments
IPFS offers bandwidth efficiency, automated replication, and faster retrieval through content based
addressing and local caching. Managers can leverage these capabilities to reduce operational costs, minimize
server load, and improve data access speeds across multiple branches or operational units. This implication
encourages decision makers to adopt distributed storage technologies to achieve higher efficiency without compromising reliability or performance.
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5.4. Integrating IPFS into Scalable Digital Transformation Initiatives
As organizations expand their digital infrastructures, scalability becomes a crucial requirement. IPFS
supports horizontal scaling by distributing data across numerous nodes without increasing dependency on centralized infrastructure. Managers can integrate IPFS into broader digital transformation strategies such as IoT
ecosystems, multi site data sharing, digital archives, or blockchain based applications to ensure future ready
systems that remain secure, verifiable, and resistant to data loss.
6.

CONCLUSION

This study confirms that the IPFS is an effective solution for maintaining data integrity in distributed
systems. Through content addressable storage and cryptographic hashes that generate CID, IPFS enables automatic data authenticity verification without depending on a central server. Simulations also demonstrate that
data access remains available even when several nodes fail, showing strong resilience.
Beyond integrity, IPFS delivers efficient bandwidth usage and good scalability. Features such as
caching, automatic replication, and selective block retrieval optimize performance under various network loads.
These strengths make IPFS suitable for modern decentralized applications requiring speed and high availability.
In blockchain environments, IPFS can support on chain verification for healthcare records, supply chain traceability, and NFT metadata storage. Integrating IPFS with consensus mechanisms such as Proof of Replication
or Proof of Spacetime may also strengthen policy oriented digital archiving systems.
Despite these advantages, several technical challenges remain. Dynamic file management, version
control, and integration with advanced security layers such as end to end encryption and blockchain based
authentication require further exploration. Future research should also include regulatory and institutional integration, for example adopting IPFS within national data governance frameworks to enhance transparency while
maintaining privacy through encrypted metadata indexing. By addressing these challenges, IPFS has the potential to become a foundational component of secure and adaptive decentralized storage systems. Overall, this
study reinforces IPFS as a reliable data integrity solution while extending its relevance to emerging blockchain
innovations and Web 3.0 infrastructure.
7.

DECLARATIONS

7.1. About Authors
https://orcid.org/0000-0003-2379-2576

Ahmad Gunawan (AG)

Mungkap Mangapul Siahaan (MM)

https://orcid.org/0000-0002-8785-1160

Rendhika Adyatama (RA)

https://orcid.org/0009-0008-7071-5374

Toktar Kerimbekov (TK)

https://orcid.org/0000-0001-6864-242X

Kristina Vaher (KV)

https://orcid.org/0009-0009-6790-0680

7.2. Author Contributions
Conceptualization: AG, MM, and RA; Methodology: TK; Software: AG; Validation: MM and RA;
Formal Analysis: TK and KV; Investigation: AG; Resources: RA; Data Curation: TK; Writing Original Draft
Preparation: KV and MM; Writing Review and Editing: AG; Visualization: MM; All authors, AG, MM, RA,
TK and KV, have read and agreed to the published version of the manuscript.
7.3. Data Availability Statement
The data presented in this study are available on request from the corresponding author.
7.4. Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
7.5. Declaration of Conflicting Interest
The authors declare that they have no conflicts of interest, known competing financial interests, or
personal relationships that could have influenced the work reported in this paper.
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