Secure Sensitive Data Sharing Using RSA and ElGamal Cryptographic Algorithms with Hash Functions

Adeniyi, Emmanuel Abidemi; Falola, Peace; Maashi, Mashael; Aljebreen, Mohammed; Bharany, Salil

doi:10.3390/info13100442

Cited by 29 publications

(26 citation statements)

References 33 publications

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“…Comparing DL + CCE with other classifiers developed in comparable and contemporary works is provided as well (see Table 7) [59][60][61][62]. The confusion matrix serves as the primary source of most of the data mining parameters.…”

Section: Discussion Comparison and Resultsmentioning

confidence: 99%

“…Other chronic conditions, such as cancer, will be tested utilizing the context-aware framework that has been presented. According to [52][53][54][55][56][57], the suggested framework will be evaluated on a variety of quality of service (QoS), energy use [58][59][60][61], and social network service (SNS) in the cloud (Cloud) criteria.…”

Section: Future Research Results and Conclusionmentioning

confidence: 99%

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Smart Patient Monitoring and Recommendation (SPMR) Using Cloud Analytics and Deep Learning

Sundas,

Badotra,

Shahi

et al. 2024

IEEE Access

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The escalating global prevalence of chronic and lifestyle-related illnesses presents substantial societal and economic challenges. This work delves into an extensive review of healthcare monitoring systems tailored for chronic and lifestyle disorders. Subsequently, we propose a pioneering Smart Patient Monitoring and Recommendation (SPMR) framework, leveraging Deep Learning (DL) and cloud-based analytics. SPMR ensures continuous monitoring and predictive insights into a patient's authentic health status using data from vital signs and contextual activities collected via Ambient Assisted Living devices. Within the predictive DL component of the LIP module, we employ Categorical Cross Entropy (CCE) Optimization to forecast real-world health conditions using unbalanced datasets derived from Chronic Blood Pressure Disorder case studies. Significantly, SPMR's capability to deliver real-time preventive measures and treatments persists even without Internet or cloud connectivity. This circumvents the need to replicate Machine Learning (ML) models and associated procedures in local setups, thus streamlining operations. Comparative analysis against analogous models showcases the considerable effectiveness of our proposed model, notably enhancing accuracy by up to 8 to 18 percent. Moreover, both the overall F-score and the emergency class F-score exhibit marked improvements of 17% and 36%, respectively. These outcomes underscore SPMR's pivotal role, especially during crises, emphasizing its significance in healthcare monitoring systems.

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Section: Discussion Comparison and Resultsmentioning

confidence: 99%

Section: Future Research Results and Conclusionmentioning

confidence: 99%

Smart Patient Monitoring and Recommendation (SPMR) Using Cloud Analytics and Deep Learning

Sundas,

Badotra,

Shahi

et al. 2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…Decryption, on the other hand, requires knowledge of the private key to recover the original message from the ciphertext [9]. Emmanuel A. Adeniyi et al [7] used RSA and ElGamal cryptographic methods, as well as SHA-256, to create digital signatures that improved security and validated the exchange of sensitive information. The study sought to authenticate shared data using SHA-256 and cryptographic techniques.…”

Section: El Gamalmentioning

confidence: 99%

The Analysis and Comparison of Various Cryptographic Algorithms to Improve Network Security: Insights from an Institutional laboratory Setup

Ganesh,

Saraf,

Chaturvedi

2024

Preprint

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In an era of growing cyber dangers, strong encryption solutions are required to maintain data confidentiality, integrity, and authenticity. After doing a thorough examination of sixteen different cryptographic algorithms and assessing parameters such as encryption/decryption timeframes and key generation efficiency. Through rigorous comparison, this paper hopes to identify the best method for improving network security in VIT Lab and similar contexts, ultimately contributing to stronger digital defence measures. This research investigates the critical role of cryptographic algorithms in improving network security in laboratory settings at Vellore Institute of Technology, Chennai.

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“…The signature will be different for each file based on the file information, which guarantees that the electronic document is reliable, the signature was created by a known source, and the document has not been altered; selected verifiers only can recover and verify the file from the digital signature [28]. The digital signature can be done using one of the public key cryptosystems, RSA or ElGamal and many other systems [29,30]. Kritsanapong et al [31] showed that RSA is 100% accurate in both signing and checking procedures, and it completed these processes with a good performance rate.…”

Section: Rsa Algorithmsmentioning

confidence: 99%

Enhancing Organizational Data Security on Employee-Connected Devices Using BYOD Policy

AlShalaan

Fati

2023

Information

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To address a business need, most organizations allow employees to use their own devices to enhance productivity and job satisfaction. For this purpose, the Bring Your Own Device (BYOD) policy provides controllable access for employees to organize data through their personal devices. Although the BYOD practice implies plenty of advantages, this also opens the door to a variety of security risks. This study investigates these security risks and proposes a complementary encryption approach with a digital signature that uses symmetric and asymmetric algorithms, depending on the organization’s digital certificate, to secure sensitive information stored in employees’ devices within the framework of BYOD policies. The method uses Advanced Encryption System (AES), Blowfish, RSA and ElGamal with a digital signature to achieve strong encryption and address critical security considerations such as user authentication, confidentiality and data integrity. The proposed encryption approach offers a robust and effective cryptographic solution for securing sensitive information in organizational settings that involve BYOD policies. The study includes experimental results demonstrating the proposed approach’s efficiency and performance, with reasonable encryption and decryption times for different key and file sizes. The results of the study revealed that AES and Blowfish have the best execution time. AES has a good balance of security and performance. RSA performs better than ElGamal in encryption and signature verification, while RSA is slower than ElGamal in decryption. The study also provides a comparative analysis with previous studies of the four encryption algorithms, highlighting the strengths and weaknesses of each approach.

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Secure Sensitive Data Sharing Using RSA and ElGamal Cryptographic Algorithms with Hash Functions

Cited by 29 publications

References 33 publications

Smart Patient Monitoring and Recommendation (SPMR) Using Cloud Analytics and Deep Learning

Smart Patient Monitoring and Recommendation (SPMR) Using Cloud Analytics and Deep Learning

The Analysis and Comparison of Various Cryptographic Algorithms to Improve Network Security: Insights from an Institutional laboratory Setup

Enhancing Organizational Data Security on Employee-Connected Devices Using BYOD Policy

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