INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)

ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue XI November 2025

Comparative Analysis of AES and Blowfish in Cloud Storage

Encryption

¹OBISESAN, Rachael Oyeranti., ²Mayowa Oyedepo Oyediran., ³Ipeayeda Funmi W., ⁴AYENI, James

Kehinde., ⁵OBISESAN, Stephen Oluwatosin

¹School of Sciences, Department of Computer Science Kwara State College of Education, Ilorin

^2,3,5Department of Computer Science, Ajayi Crowther University, Oyo Oyo State

⁴Department of Computer Science Kwara State Polytechnic

DOI: https://dx.doi.org/10.51244/IJRSI.2025.12110138

Received: 02 December 2025; Accepted: 08 December 2025; Published: 19 December 2025

ABSTRACT

Cloud storage requires efficient and secure encryption to ensure data confidentiality.This study evaluates and

compares the performance of the AES and Blowfish encryption algorithms with the aim of determining which

algorithm offers superior efficiency and reliability for secure data processing. The specific objectives are to

measure and analyze their encryption time, execution time, throughput, and Mean Square Error (MSE) across

multiple experimental runs. MATLAB was used as the primary methodology for implementing both algorithms,

generating datasets, executing repeated trials, and computing performance metrics. Execution time values were

recorded for twenty samples, where AES consistently produced lower times such as 72 s, 154 s, 95 s, 78 s, 25 s,

and a minimum of 9.1 s, while Blowfish recorded higher corresponding values including 106 s, 213 s, 138 s, 136

s, 31 s, and a minimum of 10 s. Comparative averages further showed that AES achieved a lower overall

execution range, indicating faster computational behaviour. Throughput values also demonstrated AES

superiority, with sample values above 1.00, while Blowfish maintained lower throughput levels. MSE analysis

revealed significantly lower values for AES, such as 59.88, compared to Blowfish’s much higher 126.83,

indicating better data accuracy and reduced distortion during encryption and decryption. The bar and line graph

analyses confirmed AES’s consistent performance advantage across all metrics. The results demonstrate that

AES outperforms Blowfish in terms of speed, efficiency, and reliability. In conclusion, AES is better suited for

high-performance encryption applications requiring fast execution and accurate data reconstruction. Blowfish,

although functional, shows slower and more inconsistent behaviour, making it less ideal for time-critical or high-

volume security systems.

Keywords: Cloud security, AES, Blowfish, encryption performance, Data Protection

INTRODUCTION

Cloud storage has become ubiquitous in modern information systems, offering scalable, on-demand storage and

facilitating collaboration, data sharing, and remote access across geographically distributed clients. However,

the convenience brought by cloud platforms comes with a serious concern: data confidentiality and integrity.

When sensitive data; personal information, financial records, business documents is stored in the cloud, it

becomes vulnerable to unauthorized access, interception, or breaches. Consequently, the choice of encryption

algorithm for cloud-stored data is critical for ensuring robust data protection while maintaining performance and

scalability.Symmetric-key block ciphers remain a common choice for bulk data encryption in cloud storage

because they offer a balance between security and computational efficiency. Among these, the Advanced

Encryption Standard (AES) and Blowfish algorithms are two of the most widely used. AES was standardized by

NIST and operates on 128-bit blocks with key sizes of 128, 192, or 256 bits, using a substitution–permutation

network structure. Its design emphasizes both security and performance, making it well-suited for encrypting

large volumes of data efficiently. The block-cipher rounds are optimized for fast execution and can benefit from

Page 1563

www.rsisinternational.org

INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)

ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue XI November 2025

hardware acceleration on many modern processors, a feature that is particularly beneficial when encrypting or

decrypting large files frequently stored in cloud systems (Abubakar et. al., 2025).

Blowfish, on the other hand, is a symmetric block cipher using a Feistel network with a 64-bit block size and a

variable key length of up to 448 bits. Its flexible key size and relatively simple structure have made it attractive

for applications where variable key strength or legacy compatibility matters (Khoukou, et. al., 2016). Its design

was originally motivated by the need for a fast, free alternative to proprietary ciphers and for many years, it saw

widespread adoption in software libraries prior to AES’s standardization.Despite their popularity, AES and

Blowfish differ significantly in internal design, block size, performance characteristics, and susceptibility to

certain cryptographic challenges. Such differences may translate into tangible trade-offs when these algorithms

are deployed to secure cloud storage. On one hand, AES’s larger block size (128 bits) reduces vulnerability to

block-collision–based attacks such as birthday attacks, relative to Blowfish’s 64-bit blocks. On the other hand,

Blowfish’s variable key length provides flexibility, which may be advantageous in systems requiring adjustable

security parameters; but its 64-bit block size and older design raise questions about its suitability for modern

high-volume cloud storage workloads, especially as data sizes increase and adversaries become more

sophisticated.

Empirical performance evaluations comparing AES and Blowfish across different data types support these

theoretical distinctions. For instance, a recent performance benchmarking study measuring encryption time and

throughput across image, audio, video, and textual files found that Blowfish performed efficiently for certain

file types, sometimes outperforming AES in encryption time under specific conditions though the authors noted

that performance advantages tended to diminish or even reverse as file sizes grew(Bello, et. al., 2019). Another

experimental study focusing on plain text files of varying sizes (10ꢀMB to 100ꢀMB) observed that AES

consistently outperformed Blowfish in throughput and overall encryption speed, suggesting AES’s suitability

for bulk data encryption common in cloud storage applications (Ebtihal and Elham, 2024).Beyond raw

performance, other factors influence the suitability of AES or Blowfish in a cloud context. Key management

complexity, memory usage, backward compatibility, and integration with existing cloud APIs or storage

frameworks can affect how easily and securely encryption can be deployed in real cloud storage workflows. For

example, some studies of symmetric encryption in database systems (a close analogue to cloud storage) show

that resource constraints, data access patterns, and the overhead of encryption/decryption during database

operations influence which algorithms are more practical in real-world settings(Venkatesh, et. al., 2025)

Given these trade-offs, a systematic, comparative analysis of AES and Blowfish specifically in the context of

cloud storage encryption is valuable. By evaluating both algorithms under realistic cloud storage workloads

varying file sizes, data types, frequency of encryption/decryption, and resource constraints one can derive

informed guidelines for choosing the appropriate cipher depending on the storage scenario (e.g., large file

archival vs. frequent small file access; resource-rich cloud servers vs. memory-constrained edge clients).This

paper aims to fill this gap. We conduct a comprehensive comparative study of AES and Blowfish in a cloud

storage context, focusing on performance (encryption/decryption time, throughput, memory and CPU

utilization). The findings are intended to aid practitioners and researchers in making better-informed decisions

when architecting encryption strategies for cloud storage systems, balancing security, performance, and resource

considerations.

Related Works

Bello Buhari et al. (2019) conducted a performance evaluation of symmetric encryption algorithms, focusing on

AES and Blowfish across a variety of file types including image, audio, video, and text. Their experiments

measured encryption throughput and time under different data sizes, finding that Blowfish sometimes achieved

lower encryption time than AES for particular data types and smaller workloads. However, as file sizes increased

or data complexity grew, Blowfish’s performance advantage diminished, and AES often became comparable or

superior. The authors therefore suggested that while Blowfish can be efficient for lightweight applications or

specific file types, its performance benefits are situational and may not hold for large or mixed data workloads

typical of cloud storage. System designers must evaluate file size, data type, throughput, constraints before

selecting encryption algorithm.Al-Maqtari and Al-Maqtari (2024) performed a comparative performance

assessment of AES, Blowfish, DES, and 3DES using text files ranging from 10ꢀMB to 100ꢀMB. Their results

Page 1564

www.rsisinternational.org

INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)

ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue XI November 2025

indicated that AES outperformed all other algorithms in encryption and decryption speed, especially as file size

scaled upward. Blowfish, although with flexible key lengths, exhibited slower throughput and higher latency

under large file sizes, making it less suitable for bulk data operations typical in cloud storage. The authors

concluded that AES’s consistent high performance across increasing file sizes, along with its strong security

properties, renders it the preferred choice when throughput and scalability are critical. In contrast, Blowfish

might only be recommended for legacy or low-resource contexts where high throughput is not essential. Koukou,

Othman, and Herve (2016) compared AES, Blowfish, CAST-128, and DES under different data loads,

examining encryption speed, block size, key size, avalanche effect, and data integrity using both ECB and CBC

modes. Their findings showed that AES consistently demonstrated the strongest avalanche effect and best

integrity characteristics, key security indicators across all tested conditions. Blowfish and the other algorithms

exhibited weaker diffusion and higher susceptibility to integrity issues under certain modes and data patterns.

The study thus supports AES as offering superior cryptographic robustness. While Blowfish remained

competitive in performance metrics for smaller data chunks, its weaker diffusion and block-size limitations

rendered it less desirable for high-security or large-scale encryption tasks.

Devi, et. al. (2015) studied encryption and decryption speed of DES, AES, and Blowfish specifically for image

files. They measured performance across several image sizes and concluded that Blowfish gave the lowest

encryption/decryption time among the tested algorithms for the majority of image workloads. Given that images

often constitute a large portion of user data in cloud storage (photos, scanned documents, etc.), this finding

implies that Blowfish might provide efficiency benefits for image-heavy storage scenarios. Nevertheless, the

authors cautioned that security, block size limitations, and the cipher’s relative age may pose long-term risks

thus recommending Blowfish only where speed matters more than maximal security, and AES when

confidentiality and resilience are paramount. Dhamala and Acharya (2024) explored a less common context,

DNA cryptography comparing DES, AES, and Blowfish for encoding data represented as DNA sequences. Their

work measured encryption and decryption times in this specialized environment and found that the

Blowfish-based implementation offered faster decryption times compared to AES, though encryption was slower

than with DES. While not directly related to conventional cloud storage, their results highlight Blowfish’s

potential in non-traditional data encoding contexts where decryption efficiency outweighs other factors. The

study suggests that for systems prioritizing fast retrieval or decoding (e.g., specialized storage formats), Blowfish

might be a viable candidate albeit with consideration of block size, security, and algorithmic age. Timur,

Royansyah, and Kusumaningsih (2025) conducted a contemporary comparison among AES, Blowfish, and a

modern cipher (ChaCha20) on image and document files, assessing encryption/decryption time, CPU and

memory usage, and security metrics including key strength and brute-force resistance. They reported that while

Blowfish remained faster for some smaller files, its performance degraded as file size increased — and its 64-

bit block size and older design posed limitations. AES maintained consistently high security and reliable

performance across large file sizes and mixed workloads, making it better suited for modern cloud storage

demands. The authors conclude Blowfish may be acceptable in scenarios involving small files or low resource

constraints, but AES remains the preferred cipher for large-scale, security-critical storage applications.

METHODOLOGY

A collection of chest X-ray pictures was used to test and deploy the AES and Blowfish encryption model for

COVID-19 detection. Each of the encryption methods AES and Blowfishuse a total of 20 pictures, evaluating

each method's performance in protecting medical photos, while preserving their quality after decryption was the

key goal. Basic preprocessing procedures, like resizing and format standardization, were applied to every image

to guarantee that it would work with the encryption interface. A regulated and uniform testing procedure for the

two procedures was made possible by the tests being carried out in MATLAB.The encryption and decryption

processes for the two algorithms were integrated into a MATLAB-based Graphical User Interface (GUI),

allowing users to load an image, select the desired encryption method, set the key size (128-bit or 256-bit), and

view both encrypted and decrypted outputs alongside performance metrics. The GUI also displayed critical

parameters such as encryption time, execution time, throughput, and mean squared error (MSE) for each

processed image. Figures 1 and 2 respectively illustrate the workflow and output for AES and Blowfish

respectively. These figures show the original image in the sender section, the encrypted version in the center

Page 1565

www.rsisinternational.org

INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)

ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue XI November 2025

panel, and the decrypted image in the receiver section. This design provided a visual confirmation of data

integrity after decryption, ensuring that the encryption process did not compromise image quality.

Figure 1: GUI showing the encryption and decryption process of chest X-ray image using AES algorithm.

Figure 2: GUI showing the encryption and decryption process of chest X-ray image using Blowfish algorithm.

Results with AES Algorithm

Twenty chest X-ray images were evaluated to measure the encryption performance of the AES algorithm using

mean squared error (MSE), throughput, encryption time, and execution time. Encryption times range widely,

from a minimum of 4.56 seconds (Sample 9) to a maximum of 81.62 seconds (Sample 13), indicating substantial

variability in processing demands. Execution times follow a similar pattern, spanning 9.11 to 163.07 seconds,

showing that samples with lower encryption times typically maintain proportionally lower total execution

times.Throughput values vary between 0.84 and 1.17, reflecting differences in efficiency across the runs.

Samples 6, 7, 16, and 17 exhibit the highest throughput values above 1.03, indicating more efficient data handling

relative to their execution times. Conversely, samples with higher encryption and execution durations generally

show lower throughput, such as Samples 2, 3, and 13.MSE values range from 44.49 to 84.26, measuring the

accuracy of the encryption-decryption process. Lower MSE values—observed in Samples 6, 7, 8, and 12—

indicate higher reliability and less distortion during processing. Higher MSE values, such as those in Samples 1

and 16–20, imply reduced accuracy despite moderate throughput levels. The dataset shows that samples with

Page 1566

www.rsisinternational.org

INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)

ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue XI November 2025

shorter processing times tend to achieve higher throughput and lower MSE, demonstrating an overall trend where

computational efficiency aligns with improved accuracy. This pattern highlights the importance of optimizing

both timing and algorithmic stability for enhanced encryption performance.AES demonstrated strong encryption

performance, predictable scaling with image size, and acceptable reconstruction accuracy for medical imaging

security, as summarized in Table 1.

Table 1: Performance Metrics of AES Algorithm

S/N

Encryption Time(s)

35.8342

Execution Time(s)

71.56514

153.6883

95.24788

77.50456

25.49184

21.41734

21.56393

29.71604

9.111992

97.68358

23.71878

20.61216

163.0658

19.04671

27.95603

28.40944

21.90775

20.21407

13.8484

Throughput

1.022942

0.93743

MSE

84.25871

59.75329

54.77678

57.16572

47.06714

44.49651

46.58311

63.91544

45.83622

48.04749

70.88671

67.21144

64.04633

72.80067

76.36821

67.21144

63.64636

74.62669

76.92051

47.68313

38.80568

12.76053

10.72622

10.79939

14.87443

4.561905

48.90427

11.87872

10.3199

0.926488

0.988639

0.89613

1.173255

1.16528

0.845604

0.877196

1.01192

0.864673

1.003582

0.87155

81.62309

9.535738

13.99501

14.23734

10.97007

10.12105

6.932673

28.42466

0.955651

0.932035

1.030186

1.067659

0.900462

0.931732

1.006786

56.75091

Results with Blowfish Algorithm

The Blowfish algorithm was evaluated using the same 20 chest X-ray images to assess its encryption

performance. The encryption time varies widely across the samples, ranging from a minimum of 6.78 seconds

(Sample 9) to a maximum of 141.95 seconds (Sample 2). Execution time follows a similar pattern, with the

lowest recorded value being 10.16 seconds and the highest reaching 212.63 seconds, again observed in Sample

Page 1567

www.rsisinternational.org

INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)

ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue XI November 2025

2. These variations indicate differing computational loads or data conditions across the trials. Throughput values

show moderate fluctuations, spanning from 0.56 (Sample 4) to 0.79 (Samples 9 and 10). Higher throughput

values generally correspond to lower encryption and execution times, suggesting increased efficiency in

processing data. Samples 5, 9, 10, 16, and 17 exhibit relatively strong throughput performance, indicating

efficient data handling. The MSE values range considerably, from 78.53 (Sample 7) to 205.56 (Sample 18).

Lower MSE implies greater accuracy and reliability of the encryption-decryption cycle. Only a few samples fall

below 100, such as Samples 5, 6, 7, 9, 11, and 12, demonstrating superior accuracy. Samples with significantly

high MSE, like Sample 18, indicate greater deviation and reduced reliability. Overall, the dataset reflects

substantial performance variability across the 20 samples. Trials with lower encryption and execution times tend

to achieve higher throughput and better MSE scores, highlighting a general inverse relationship between

processing duration and efficiency. These insights can guide optimization efforts toward faster and more accurate

encryption performance. Blowfish displayed strong security strength but produced higher reconstruction errors

and slower processing compared to AES, as shown in Table 2.

Table 2: Performance Metrics of Blowfish Algorithm

S/N Encryption Time (s)

Execution Time (s)

105.7807440

212.6322268

138.4396153

136.1604269

31.40302054

38.48263499

77.56875830

94.96062508

10.15604882

125.2334023

32.46946922

35.20527554

206.1405463

31.99197929

43.81402627

40.39857481

32.34680830

18.66782498

90.94501980

Throughput

MSE

File Size (bytes)

70.63623918

141.9516836

92.50156154

90.86251710

20.97629901

25.68989881

51.78137075

63.38420670

6.783158701

83.62566534

21.69213156

23.51782273

137.8167865

21.36304980

29.26595751

26.98225827

21.62433166

12.46720842

60.74623200

0.692063576

0.677564272

0.637433150

0.562747942

0.727445947

0.652969840

0.653781253

0.574216945

0.787018667

0.789310186

0.631639522

0.587582392

0.689432538

0.568955107

0.594695403

0.724456250

0.723100708

0.669708443

0.689185622

168.5174203 73207

119.5065893 144072

109.5535630 88246

114.3314394 76624

94.13427241 22844

88.99301610 25128

78.52801452 50713

138.0716738 54528

93.16621291 7993

127.8308870 98848

91.67244713 20509

96.09497317 20686

141.7734178 142120

134.4228812 18202

128.0926649 26056

145.6013455 29267

152.7364268 23390

205.5625000 12502

154.0465988 62678

Page 1568

www.rsisinternational.org

INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)

ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue XI November 2025

61.66466258

92.24641788

0.679462698

154.0465988 62678

Comparison Results of the Encrypted Algorithms

The performance comparison between AES and Blowfish Table 3 reveals notable differences across encryption

time, execution time, throughput, and error levels. AES demonstrates significantly faster encryption, recording

24.99543 seconds, whereas Blowfish requires 53.26665 seconds, indicating that AES encrypts data more

efficiently. A similar trend appears in execution time, where AES completes its full cycle in 49.92603 seconds,

compared to Blowfish’s slower 79.75217 seconds, reinforcing AES’s superior speed. Throughput results further

strengthen this observation. AES attains a throughput of 0.97046, meaning it processes data at a higher rate than

Blowfish, which achieves only 0.665639. Higher throughput translates to better performance in applications

requiring rapid data handling. Error measurement using Mean Square Error (MSE) shows AES at 59.88456,

which is considerably lower than Blowfish’s 126.8341. A lower MSE signifies higher accuracy and better overall

reliability in maintaining data quality during encryption and decryption processes. In conclusion, the results

consistently confirm that AES outperforms Blowfish in all assessed categories. AES is faster, more efficient,

and more accurate, making it better suited for systems demanding high speed, reliability, and strong encryption

performance. Blowfish, while functional, lags significantly behind AES in terms of processing speed and

accuracy, limiting its suitability for high-performance security environments.

Table 3: Mean Performance Metrics of the two encrypted Algorithms

Algorithms

AES

Encryption Time (s)

24.99543

Execution Time (s)

49.92603

Throughput

0.97046

MSE

59.88456

126.8341

53.26665

79.75217

0.665639

Blowfish

140

120

100

AES

Blowfish

Encryption Execution Throughput

Time (s) Time (s)

MSE

Figure 3:Bar graph showing the comparison of AES and Blowfish algorithms

Figure 3 compares AES and Blowfish across four performance metrics: encryption time, execution time,

throughput, and MSE. AES shows lower encryption and execution times than Blowfish, indicating faster

processing. In throughput, AES slightly outperforms Blowfish, demonstrating better data-handling efficiency.

The most significant difference appears in MSE, where Blowfish records a much higher value, suggesting greater

inaccuracy or data distortion during encryption and decryption. Overall, the graph indicates that AES performs

more efficiently and reliably across all metrics, making it the superior algorithm in terms of speed, throughput,

and accuracy.

Page 1569

www.rsisinternational.org

INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)

ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue XI November 2025

Graph of Execution Time Evaluation for AES and Blowfish

250

200

150

100

10 11 12 13 14 15 16 17 18 19 20

AES

Blowfish

72 154 95 78 25 21 22 30 9.1 98 24 21 163 19 28 28 22 20 14 57

106213138136 31 38 78 95 10 125 32 35 206 32 44 40 32 19 91 92

Figure 4:Line graph showing the comparison of AES and Blowfish algorithms

Figure 4 shows the comparison of the execution times of AES and Blowfish across 20 test samples. Overall,

AES consistently performs faster, showing lower execution times in most samples. Blowfish displays higher

peaks, particularly at samples 2, 12, and 19, indicating slower and more unstable performance. Both algorithms

share a similar pattern in fluctuations, but Blowfish’s values remain generally higher. AES demonstrates greater

stability and efficiency, maintaining lower execution times throughout the evaluation. This suggests that AES is

more suitable for applications requiring faster and more predictable execution performance.

CONCLUSION

The AES algorithm shows moderate execution times across the samples, with values fluctuating between

approximately 9 and 163 seconds. This performance reflects AES’s design balance between security and speed,

where its efficient key scheduling and encryption rounds enable relatively fast processing compared to other

algorithms. Despite some fluctuations that AES is still a popular and reliable encryption option, regardless of

input variations or system conditions, utilized in a variety of applications because of its dependability and steady

pace. Blowfish consistently records the highest execution times among the algorithms tested, with values ranging

from around 10 to over 212 seconds. This elevated processing time is attributable to Blowfish’s more complex

key expansion and encryption structure, which imposes higher computational demands. The trade-off for this

complexity is generally enhanced security, but it comes at the cost of slower encryption speeds. As such,

Blowfish may be less suitable for applications requiring rapid data handling or real-time encryption, especially

for large datasets.In conclusion, AES is faster, more efficient, and more accurate, making it better suited for

systems demanding high speed, reliability, and strong encryption performance. Blowfish, while functional, lags

significantly behind AES in terms of processing speed and accuracy, limiting its suitability for high-performance

security environments.

REFERENCES

1. Abubakar Z. M., Bala M., Mohammed U. & Ahmed. M. K. (2025). Application of Advanced Encryption

Standard

(AES)

for

Securing

Electronic

Banking

Transactional

Data.DOI: https://doi.org/10.51584/IJRIAS.2025.100800074

2. Al-Maqtari, E. A., & Al-Maqtari, E. A. (2024).Performance evaluation for AES, Blowfish, DES, and

3DES cryptography algorithms.Partners Universal Innovative Research Publication, 2(5), 86–

95.https://doi.org/10.5281/zenodo.13974870

Page 1570

www.rsisinternational.org

INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)

ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue XI November 2025

3. Bello Buhari, A., AfolayanAyodeleObiniyi, A., Kissinger, S., &Sirajo, S. (2019). Performance

evaluation of symmetric data encryption algorithms: AES and Blowfish. Saudi Journal of Engineering

and Technology (SJEAT), 4(10), 407–414.https://doi.org/10.36348/SJEAT.2019.v04i10.002

4. Devi, A., Sharma, A., &Rangra, A. (2015). Performance analysis of symmetric key algorithms: DES,

AES and Blowfish for image encryption and decryption. International Journal of Engineering and

Computer Science, 4(06).

5. Dhamala, N., & Acharya, K. P. (2024). A comparative analysis of DES, AES and Blowfish based DNA

cryptography. Adhyayan Journal, 11(11), 69–80.https://doi.org/10.3126/aj.v11i11.67080

6. Ebtihal A. A.&Elham A. A. (2024).PerformanceEvaluation for AES, Blowfish, DES, and 3DES

Cryptography Algorithms. Partners Universal Innovative Research Publication (PUIRP), 02(05), 86–

95. https://doi.org/10.5281/zenodo.13974870

7. Koukou, Y. M., Othman, S. H., &HerveNkiama, M. M. (2016).Comparative study of AES, Blowfish,

CAST-128

and

DES

encryption

algorithm.IOSR

Journal

Engineering,

6(6),

1–

7.https://doi.org/10.9790/3021-066010107

8. Timur, M. B. B., Royansyah, R., &Kusumaningsih, D. (2025).Comparison of efficiency and security of

AES, Blowfish, and ChaCha20 cryptographic algorithms on image and document files.Innovatics

Journal, 7(2).

9. Venkatesh V, Swathi L, Tangudu N. and Satishkumar, E S. (2025).Implementation and Evaluation of

Data Protection in Databases Using Symmetric Encryption Algorithms.J Neonatal Surg [Internet]. 2025,

Mar.24

[cited

2025Nov.30];14(8S):440-59.

Available

from:

https://www.jneonatalsurg.com/index.php/jns/article/view/2558 DOI: https://doi.org/10.52783/jns.v14.2

558

Page 1571