Overview of Blockchain Oracle Research

Caldarelli, Giulio

doi:10.20944/preprints202109.0135.v2

“…Solutions have been proposed to address interactions between a Blockchain and the external environment. Caldarelli [10] provides an overview of the research on oracles, which allow the use of off-chain data in smart contracts. To the best of our knowledge, oracles are not capable of reliably pushing data out of the Blockchain, as required in our case.…”

Section: Related Workmentioning

confidence: 99%

Modeling Trust Relationships in Blockchain Applications: The Case of Reconfigurable Systems-on-Chip

Méré¹,

Jouault²,

Perdriau³

et al. 2022

Preprint

0

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<p>Nowadays, System-on-Chip (SoC) components are found everywhere in all kinds of smart devices. Each System-on-Chip contains many different blocks that provide specific functionalities, such as WiFi or Bluetooth connectivity. Whereas integrating each such block in a SoC typically requires paying some royalties, not all blocks are necessary for all applications, or throughout a device’s lifecycle. Moreover, it is not possible to manufacture a specific SoC for each application. Significant advantages are therefore expected to be gained by enabling trustworthy remote SoC reconfiguration throughout their life cycles. A few approaches attempting to address this challenge have been proposed in the literature. They are typically based on Blockchain technology in order to support decentralization without relinquishing trust. Reviewing these approaches lead us to identify a potential flaw in the proposed protocols. Indeed, a SoC should be able to trust Blockchain information that it is given, without requiring any centralization. In order to validate our suspicions, we propose in this paper to use Verifpal: a cryptographic protocol verification tool that works from textual protocol models. We use it in a slightly unorthodox way in order to model the trust relationships in one of the approaches from the literature, and to verify it. The results show that, under some assumptions, a flaw is indeed present. We propose and model several possible fixes, and present their respective limitations. </p>

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Blockchain Transaction Fee Forecasting: A Comparison of Machine Learning Methods

Butler

¹

,

Crane

²

2023

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Gas is the transaction-fee metering system of the Ethereum network. Users of the network are required to select a gas price for submission with their transaction, creating a risk of overpaying or delayed/unprocessed transactions involved in this selection. In this work, we investigate data in the aftermath of the London Hard Fork and shed insight into the transaction dynamics of the network after this major fork. As such, this paper provides an update on work previous to 2019 on the link between EthUSD/BitUSD and gas price. For forecasting, we compare a novel combination of machine learning methods such as Direct-Recursive Hybrid LSTM, CNN-LSTM, and Attention-LSTM. These are combined with wavelet threshold denoising and matrix profile data processing toward the forecasting of block minimum gas price, on a 5-min timescale, over multiple lookaheads. As the first application of the matrix profile being applied to gas price data and forecasting that we are aware of, this study demonstrates that matrix profile data can enhance attention-based models; however, given the hardware constraints, hybrid models outperformed attention and CNN-LSTM models. The wavelet coherence of inputs demonstrates correlation in multiple variables on a 1-day timescale, which is a deviation of base free from gas price. A Direct-Recursive Hybrid LSTM strategy is found to outperform other models, with an average RMSE of 26.08 and R2 of 0.54 over a 50-min lookahead window compared to an RMSE of 26.78 and R2 of 0.452 in the best-performing attention model. Hybrid models are shown to have favorable performance up to a 20-min lookahead with performance being comparable to attention models when forecasting 25–50-min ahead. Forecasts over a range of lookaheads allow users to make an informed decision on gas price selection and the optimal window to submit their transaction in without fear of their transaction being rejected. This, in turn, gives more detailed insight into gas price dynamics than existing recommenders, oracles and forecasting approaches, which provide simple heuristics or limited lookahead horizons.

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Modeling Trust Relationships in Blockchain Applications: The Case of Reconfigurable Systems-on-Chip

Méré¹,

Jouault²,

Perdriau³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

<p>Nowadays, System-on-Chip (SoC) components are found everywhere in all kinds of smart devices. Each System-on-Chip contains many different blocks that provide specific functionalities, such as WiFi or Bluetooth connectivity. Whereas integrating each such block in a SoC typically requires paying some royalties, not all blocks are necessary for all applications, or throughout a device’s lifecycle. Moreover, it is not possible to manufacture a specific SoC for each application. Significant advantages are therefore expected to be gained by enabling trustworthy remote SoC reconfiguration throughout their life cycles. A few approaches attempting to address this challenge have been proposed in the literature. They are typically based on Blockchain technology in order to support decentralization without relinquishing trust. Reviewing these approaches lead us to identify a potential flaw in the proposed protocols. Indeed, a SoC should be able to trust Blockchain information that it is given, without requiring any centralization. In order to validate our suspicions, we propose in this paper to use Verifpal: a cryptographic protocol verification tool that works from textual protocol models. We use it in a slightly unorthodox way in order to model the trust relationships in one of the approaches from the literature, and to verify it. The results show that, under some assumptions, a flaw is indeed present. We propose and model several possible fixes, and present their respective limitations. </p>

show abstract

Overview of Blockchain Oracle Research

Cited by 4 publications

References 89 publications

Modeling Trust Relationships in Blockchain Applications: The Case of Reconfigurable Systems-on-Chip

Modeling Trust Relationships in Blockchain Applications: The Case of Reconfigurable Systems-on-Chip

Blockchain Transaction Fee Forecasting: A Comparison of Machine Learning Methods

Modeling Trust Relationships in Blockchain Applications: The Case of Reconfigurable Systems-on-Chip

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