On Using zk-SNARKs and zk-STARKs in Blockchain-Based Identity Management

Panait, Andreea-Elena; Olimid, Ruxandra F.

doi:10.1007/978-3-030-69255-1_9

Cited by 19 publications

(9 citation statements)

References 31 publications

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“…Zero‐knowledge‐Succinct Non‐Interactive Argument of Knowledge (zk‐SNARKs) is another significant type of ZKP that has recently developed. The zk‐SNARK is the most popular among the zero‐knowledge proof alternatives 142 . It can assist in the definition of a quadratic equation that uses public, private, and input data to generate evidence.…”

Section: Discussionmentioning

confidence: 99%

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Securing AI‐based healthcare systems using blockchain technology: A state‐of‐the‐art systematic literature review and future research directions

Shinde,

Patil,

Kotecha

et al. 2023

Trans Emerging Tel Tech

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Healthcare institutions are progressively integrating artificial intelligence (AI) into their operations. The extraordinary potential of AI is restricted by insufficient medical data for AI model training and adversarial attacks wherein attackers perturb the dataset by adding some noise to it, which leads to the malfunctioning of the AI models, and a lack of trust caused by the opaque operational approach it employs. This Systematic Literature Review (SLR) is a state‐of‐the‐art survey of the research on blockchain technology for securing AI‐integrated healthcare applications. The most relevant articles from the Scopus and Web of Science (WoS) databases were identified using the PRISMA model. Most of the existing literature is about protecting the healthcare data used by AI‐based healthcare systems using blockchain technology, but the modality of data (text, images, audio, and sound) was not specifically mentioned. Information on protecting the training phase and model deployment for AI‐based healthcare systems considering the variations in feature extraction based on the modality of data was also not clearly specified. Hence, the three subfields of AI, namely, natural language processing (NLP), computer vision, and acoustic AI are further studied to identify security loopholes in its implementation pipeline. The three phases, namely the dataset, the training phase, and the trained models need to be protected from adversaries to avoid malfunctioning of the deployed AI models. The nature of the data processed by NLP, computer vision, and acoustic AI, underlying deep neural network (DNN) architectures, the complexity of attacks, and the perceivability of attacks by humans are analyzed to identify the need for security. A blockchain solution for AI‐based healthcare systems is synthesized based on the findings that have demonstrated the distinctive technological features of blockchains. It offers a solution for the privacy and security issues encountered by NLP, computer vision, and acoustic AI to boost the widespread adoption of AI applications in healthcare.

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Section: Discussionmentioning

confidence: 99%

“…The zk-SNARK is the most popular among the zero-knowledge proof alternatives. 142 It can assist in the definition of a quadratic equation that uses public, private, and input data to generate evidence.…”

Section: Hyperledgermentioning

confidence: 99%

Securing AI‐based healthcare systems using blockchain technology: A state‐of‐the‐art systematic literature review and future research directions

Shinde,

Patil,

Kotecha

et al. 2023

Trans Emerging Tel Tech

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“…Finally, the last author featured in Table V is Andreea-Elena Panait, with an h-index of 2, a g-index of 2, and 9 citations. Her research topics are security and privacy (51); zero-knowledge proofs (49); blockchain and digital identity (48); and frameworks for SSI (50). In summary, the top 15 authors on SSI can be grouped according to their collaborations.…”

Section: Bibliometric Analysis Of Authorsmentioning

confidence: 99%

A Bibliometric Study of Scientific Production on Self-Sovereign Identity

Diaz

Méndez²,

Vasquez³

2023

Ing.

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Context: Self-sovereign identity (SSI) enables the creation of user-centric, privacy-by-design, secure, and decentralized identity-management systems. The aim of this paper is to carry out a bibliometric analysis of the scientific production on SSI during the period 2017-2022.Method: A complete bibliometric analysis of all publications on SSI indexed in Scopus and Web of Science was carried out. A corpus of 143 articles was examined by processing their bibliographic metadata on a bibliometric tool. In order to do this, the Bibliometrix packageand the R programming language were used.Results: A bibliometric characterization of the publications on SSI was obtained for the 2017 − 2022 period. The most important keywords used in these publications were identified, as well as their use tendencies throughout this period. Moreover, the most influential authors in the area and the most relevant publication sources were also identified.Conclusions: The results of the bibliometric analysis show that Lotka’s and Bradford’s laws apply for academic publications on SSI, which means that the most relevant publications in this area are concentrated in a relatively small group of authors and journals. Paul Jenkins, Nitin Naik, Yang Liu and Aijun An were the most impactful authors, and Lecture notes in computer science, Frontiers in blockchain and IEEE were the most influential journals. Finally, the keyword analysis showed that Blockchain, Authentication, Identity management, Electronic document identification systems, and Digital identity are currently the most relevant concepts for SSI.

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“…However, concerns of excessive energy consumption, low throughput, and high resource consumption associated with the existing blockchain networks necessitate further research to improve the scalability and efficiency of these distributed networks. Thankfully, much research is being performed to address these challenges using novel approaches such as zero-knowledge proof transaction offloading (Ashur and Dhooghe, 2018;Panait and Olimid, 2020;Ben-Sasson et al, 2019), and lighter-weight consensus schemes (Guo et al, 2021a) designed for location-dependent networks (e.g., IoV).…”

Section: Blockchain-enabled Traffic Predictionmentioning

confidence: 99%