Zilch: A Framework for Deploying Transparent Zero-Knowledge Proofs

Mouris, Dimitris; Tsoutsos, Nektarios Georgios

doi:10.1109/tifs.2021.3074869

Cited by 25 publications

(6 citation statements)

References 51 publications

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“…ZKPs enable a prover to assert knowledge of a statement without the need to divulge supplementary information [36]. Prominent ZKP protocols encompass zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge) [37] and zk-STARKs (Zero-Knowledge Scalable Transparent Arguments of Knowledge) [38]. These ZKPs nd applications across diverse domains, such as anonymous transactions, identity management, and data con dentiality.…”

Section: Conclusion Of Bibiliometric Analysismentioning

confidence: 99%

A Comprehensive Survey Integrating Scientometric Analysis and ML approaches for Data Protection

Yaseen,

Hassan

2024

Preprint

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The extensive implementation of smart contracts in blockchain ecosystems has brought significant benefits, particularly in enhancing transparency and transactional effectiveness. However, this remarkable advancement has also raised substantial concerns regarding the protection of sensitive data during smart contract execution. This research analysis delves into an in-depth examination of the constantly evolving domain of data privacy in the realm of smart contracts, going beyond the inherent transparency that defines blockchain technology. In this pursuit, we embark on an exploration of existing literature through a bibliometric analysis, shedding light on the trends and developments in this crucial field of study. Our objective is to discern the key challenges and issues confronting data privacy. Through a systematic bibliometric analysis, we aim to identify and evaluate the efficacy of current privacy-preserving mechanisms while pinpointing potential avenues for future research endeavors to address privacy concerns in smart contracts comprehensively. This literature review, informed by bibliometric insights, not only advances our collective understanding of data privacy within smart contracts but also offers invaluable guidance to developers, researchers, and policymakers seeking to shape and implement privacy-enhancing measures in forthcoming blockchain-based systems.

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Section: Conclusion Of Bibiliometric Analysismentioning

confidence: 99%

A Comprehensive Survey Integrating Scientometric Analysis and ML approaches for Data Protection

Yaseen,

Hassan

2024

Preprint

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“…The most basic forms of hyperparameter search are the random and grid varieties [24]. Without doing any optimisation, both methods look for hyperparameters that fall inside predetermined bounds.…”

Section: Automatic Search For Hyperparametermentioning

confidence: 99%

Deep Learning Method for Power Side-Channel Analysis on Chip Leakages

Ahmed,

Salim,

Hasan

2023

ELEKTRON ELEKTROTECH

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Power side channel analysis signal analysis is automated using deep learning. Signal processing and cryptanalytic techniques are necessary components of power side channel analysis. Chip leakages can be found using a classification approach called deep learning. In addition to this, we do this so that the deep learning network can automatically tackle signal processing difficulties such as re-alignment and noise reduction. We were able to break minimally protected Advanced Encryption Standard (AES), as well as masking-countermeasure AES and protected elliptic-curve cryptography (ECC). These results demonstrate that the attacker knowledge required for side channel analysis, which had previously placed a significant emphasis on human abilities, is decreasing. This research will appeal to individuals with a technical background who have an interest in deep learning, side channel analysis, and security.

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“…This vulnerability usually was performed to carry out attacks against the authorization of the IoT devices which allows the attacker to gain permission to control and access its ecosystem [19][20][21][22][23].…”

Section: Vulnerabilities Impact (Consequences)mentioning

confidence: 99%

Machine-Learning-Based Vulnerability Detection and Classification in Internet of Things Device Security

Hulayyil,

Li,

2023

Electronics

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Detecting cyber security vulnerabilities in the Internet of Things (IoT) devices before they are exploited is increasingly challenging and is one of the key technologies to protect IoT devices from cyber attacks. This work conducts a comprehensive survey to investigate the methods and tools used in vulnerability detection in IoT environments utilizing machine learning techniques on various datasets, i.e., IoT23. During this study, the common potential vulnerabilities of IoT architectures are analyzed on each layer and the machine learning workflow is described for detecting IoT vulnerabilities. A vulnerability detection and mitigation framework was proposed for machine learning-based vulnerability detection in IoT environments, and a review of recent research trends is presented.

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Zilch: A Framework for Deploying Transparent Zero-Knowledge Proofs

Cited by 25 publications

References 51 publications

A Comprehensive Survey Integrating Scientometric Analysis and ML approaches for Data Protection

A Comprehensive Survey Integrating Scientometric Analysis and ML approaches for Data Protection

Deep Learning Method for Power Side-Channel Analysis on Chip Leakages

Machine-Learning-Based Vulnerability Detection and Classification in Internet of Things Device Security

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