Design of a cryptographically secure pseudo random number generator with grammatical evolution

Ryan, Conor; Kshirsagar, Meghana; Vaidya, Gauri; Cunningham, Andrew; Sivaraman, R.

doi:10.1038/s41598-022-11613-x

Cited by 27 publications

(12 citation statements)

References 24 publications

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“…This mapping separates the genotype (binary string) from the phenotype (executable program or expression tree), making it possible to incorporate grammar constraints in the search procedure. GE has been used in various applications, such as parameter optimisation for time series modeling [34], cryptography [35], etc., due to its flexibility in defining search spaces using BNF grammar.…”

Section: Grammatical Evolutionmentioning

confidence: 99%

Grammatical Evolution-Driven Algorithm for Efficient and Automatic Hyperparameter Optimisation of Neural Networks

2023

Self Cite

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Neural networks have revolutionised the way we approach problem solving across multiple domains; however, their effective design and efficient use of computational resources is still a challenging task. One of the most important factors influencing this process is model hyperparameters which vary significantly with models and datasets. Recently, there has been an increased focus on automatically tuning these hyperparameters to reduce complexity and to optimise resource utilisation. From traditional human-intuitive tuning methods to random search, grid search, Bayesian optimisation, and evolutionary algorithms, significant advancements have been made in this direction that promise improved performance while using fewer resources. In this article, we propose HyperGE, a two-stage model for automatically tuning hyperparameters driven by grammatical evolution (GE), a bioinspired population-based machine learning algorithm. GE provides an advantage in that it allows users to define their own grammar for generating solutions, making it ideal for defining search spaces across datasets and models. We test HyperGE to fine-tune VGG-19 and ResNet-50 pre-trained networks using three benchmark datasets. We demonstrate that the search space is significantly reduced by a factor of 90% in Stage 2 with fewer number of trials. HyperGE could become an invaluable tool within the deep learning community, allowing practitioners greater freedom when exploring complex problem domains for hyperparameter fine-tuning.

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Section: Grammatical Evolutionmentioning

confidence: 99%

Grammatical Evolution-Driven Algorithm for Efficient and Automatic Hyperparameter Optimisation of Neural Networks

2023

Self Cite

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“…Most of the works have either used blockchain for EHRs or KGs, but no work has used both for EHRs and data‐sharing to the best of our knowledge. All the works such as in References 36,37 have focused on reducing the chances of data tampering while increasing the data security and efficiency of the system through the use of blockchain and cryptographic algorithms. With this objective, the data storage is either private servers or secured clouds, or a combination of off‐chain and on‐chain storage.…”

Section: Related Workmentioning

confidence: 99%

SENSIBLE: SEquestered aNd SynergIstic BLockchain Ecosystem

Kshirsagar

Vaidya

Yao

et al. 2022

Engineering Reports

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Health care interoperability unfolds the way for personalized health care services at a reduced cost. Furthermore, a decentralized system holds the promise to prevent compromises such as cyber-attacks due to data breaches. Hence, there is a need for a framework that seamlessly integrates and shares data across the system stakeholders. We propose SEquestered aNd SynergIstic BLockchain Ecosystem (SENSIBLE), a blockchain-powered, knowledge-driven data-sharing framework that gives patients complete control of their medical history and can extract rich information hidden in it using knowledge graphs (KGs). By incorporating both blockchain and KGs, we can provide a platform for secure data sharing among stakeholders by maintaining data privacy and integrity through data authentication and robust data integration. We present a Proof-of-Concept of the SENSIBLE network with Ethereum to share dynamic knowledge across stakeholders. Dynamic knowledge generation on the blockchain provides a two-fold advantage of cooperation and communication amongst the stakeholders in the health care ecosystem. This leads to operational ease through sharing relevant portions of complex information while also ensuring the isolation of sensitive medical data.

show abstract

“…Most of the works have either used blockchain for EHRs or KGs, but no work has used both for EHRs and data-sharing to the best of our knowledge. All the works such as in [36,37] have focused on reducing the chances of data tampering while increasing the data security and efficiency of the system through the use of blockchain and cryptographic algorithms. With this objective, the data storage is either private servers or secured clouds, or a combination of off-chain and on-chain storage.…”

Section: Related Workmentioning

confidence: 99%

SENSIBLE: Sequestered and Synergistic Blockchain Ecosystem

Kshirsagar

Vaidya

Yao

et al. 2022

Preprint

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Healthcare interoperability unfolds the way for personalized healthcare services at a reduced cost. Furthermore, a decentralized system holds the promise to prevent compromises such as cyber-attacks due to data breaches. Hence, there is a need for a framework that seamlessly integrates and shares data across the system stakeholders. We propose SENSIBLE, a blockchain-powered, knowledge-driven data-sharing framework that gives patients complete control of their medical history and can extract rich information hidden in it using knowledge graphs (KGs). By incorporating both blockchain and KGs, we can provide a platform for secure data sharing amongst stakeholders by maintaining data privacy and integrity through data authentication and robust data integration. We present a Proof-of-Concept of the SENSIBLE network with Ethereum to share dynamic knowledge across stakeholders. Dynamic knowledge generation on the blockchain provides a two-fold advantage of cooperation and communication amongst the stakeholders in the healthcare ecosystem. This leads to operational ease through sharing relevant portions of complex information while also ensuring the isolation of sensitive medical data.

show abstract

Design of a cryptographically secure pseudo random number generator with grammatical evolution

Cited by 27 publications

References 24 publications

Grammatical Evolution-Driven Algorithm for Efficient and Automatic Hyperparameter Optimisation of Neural Networks

Grammatical Evolution-Driven Algorithm for Efficient and Automatic Hyperparameter Optimisation of Neural Networks

SENSIBLE: SEquestered aNd SynergIstic BLockchain Ecosystem

SENSIBLE: Sequestered and Synergistic Blockchain Ecosystem

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