With technology brazenly breaching through society’s barriers in countless diverse fields, the 21st century has revolutionized many age-old industries. One of the largest areas within society influenced by the progress of technology are the fields of finances, economy, and investment, coupled with the aspect of social influence. With this shift in society parallel to the advancement of contemporary technologies, thus becoming increasingly reliant on the tools made available, the complex yet traditional world of finance has pivoted, becoming contingent upon the use of cutting-edge technologies. This critical shift has introduced the world of Fintech, providing many innovative fiscal opportunities. The coined term, Fintech, is a general term referring to products as services for fiscal activities developed by entities unrelated to banks, insurance firms, nor online companies, providing alternatives to the traditional options available to the general public. (Gulamhuseinwala, Bull and Lewis, 2015). Resultantly of this Fintech trend, one of the largest and most promising fields of contemporary investment is recognized as that of Cryptocurrencies, with Bitcoin and Ethereum being the most recognized and heavily traded currencies. Many positive traits can be used to define the novelty of this new economy, with one of the main aspects being its peer to peer (P2P) nature of its trading process. However, beyond the tremendous advancements visible within the process of Cryptocurrency production and trade, one of the most important aspects is the influencers upon the valuation of the different currencies. Similarly to the progression of the economy which has transported the financial world to a digital economy, so has the social world, advancing discourse regarding many topics to the online environment. Thus, it is critical to analyze and assess the nature of online discourse regarding Cryptocurrencies. Explicitly, the chatter preceding to sharp rises and falls with Bitcoin and Ethereum, the most recognizable coins. Additionally, it is imperative to appraise the trends in the behavior and quantity of online discourse prior to a significant drop in Ethereum & Bitcoin.Within the online arena, there are countless different outlets and platforms for people to express themselves in general, or more importantly in this instance regarding the topic of Cryptocurrencies. By using platforms that are designated for discussion regarding financial topics or general social media platforms, the public is provided a critical platform utilized by countless individuals, many of whom are increasingly involved with the aforementioned topics. These platforms stipulate a stage for these individuals, who have become critical by voicing their opinions, thoughts, and experiences. Many of these influencers are sought after for their knowledge, specifically influencing the behavior of others. However, it is critical to evaluate the importance of additional aspects beyond the superficial such as who are the influencers, rather elements such as the content or nature of what is being discussed. Resultantly to trends of content, nature, and volume of what is being discussed in the online arena., people’s behavior regarding investments, specifically within Cryptocurrencies, are very possibly subjected to the influence of others, leading to rises and falls in coin valuation.
The General Purpose Atomic Crosschain Transaction protocol allows composable programming across multiple Ethereum blockchains. It allows for inter-contract and inter-blockchain function calls that are both synchronous and atomic: if one part fails, the whole call execution tree of function calls is rolled back. The protocol operates on existing Ethereum blockchains without modification. It works for both public permissioned and consortium blockchains. Additionally, the protocol is expected to work across heterogeneous blockchains other than Ethereum. This paper describes the protocol, analyses it in terms of Gas usage and Finalised Block Periods for three scenarios: reading a value from one blockchain to another, writing a value from one blockchain to another, and a trade finance system involving five contracts on five blockchains with a complex call execution tree, and provides an initial security analysis that shows that the protocol has Safety and Liveness properties.
Mirko Zichichi, Stefano Ferretti, Gabriele D’Angelo, Victor Rodrı́guez-Doncel
This paper presents an architecture of a Personal Information Management System, in which individuals can define the access to their personal data by means of smart contracts. These smart contracts, running on the Ethereum blockchain, implement access control lists and grant immutability, traceability and verifiability of the references to personal data, which is stored itself in a (possibly distributed) file system. A distributed authorization mechanism is devised, where trust from multiple network nodes is necessary to grant the access to the data. To this aim, two possible alternatives are described: a Secret Sharing scheme and Threshold Proxy Re-Encryption scheme. The performance of these alternatives is experimentally compared in terms of execution time. Threshold Proxy Re- Encryption appears to be faster in different scenarios, in particular when increasing message size, number of nodes and the threshold value, i.e. number of nodes needed to grant the data disclosure.
The present study is on the five cryptocurrency daily mean return time series linearity dynamics during the Covid-19 period. These cryptocurrencies were chosen based on their influence on the market, primarily driven by its market capitalisation. Tether is included as the most important stable coin on the market, nominally pegged to the U.S. dollar (USD). The reason to investigate it is that there are some inconsistencies in its behaviour as opposed to the other four cryptocurrencies. This study found that the behaviour of Tether cryptocurrency daily average return time series pattern is highly nonlinear and chaotic in nature, whereas the other four cryptocurrencies (namely Bitcoin, Ethereum, XRP and Bitcoin Cash) daily average return time series were found to be linear in nature. To further study Tether’s nonlinear time series rich dynamics, this study deployed one category of the regime switching models popularly known as the threshold regressions. The study estimates fairly suggest that both the threshold autoregression (TAR) and smooth transition autoregressive (STAR) models with lag 1 are adequate to capture the rich nonlinear and chaotic dynamics of Tether’s daily average return time series.
Rana Mahmoud Abdou, Hamdy Mahmoud Kadous, Ahmed Elsayed Hamdallah
The rapid development of cryptocurrencies has attracted the attention of investors, speculators, regulators, and academics in recent years. A large amount of research has been devoted to the pricing mechanisms of cryptocurrency markets, the drivers of volatility and the diversification potential of cryptocurrencies. (Kurka, 2019, P.38). bitcoin is the best-known cryptocurrency which currently holds the largest market capitalization and is regarded as a standard example of a cryptocurrency.( Jaywant, 2019 , P.147) bitcoin has emerged as the most popular virtual currency, and maintains the greatest share ahead of its competitors; the Ethereum, Ripple, Litecoin and bitcoin Cash. (Vardar & Aydogan , 2019 , P.2 )جذب التطور السريع للعملات المشفرة انتباه المستثمرين والمضاربين والمنظمين والأکاديميين في السنوات الأخيرة. تم تخصيص قدر کبير من الأبحاث لآليات التسعير الخاصة بأسواق العملات المشفرة ، ومحرکات التقلب وإمکانية التنويع في العملات المشفرة. عملة البيتکوين هي العملة المشفرة الأکثر شهرة والتي تمتلک حاليًا أکبر قيمة سوقية وتعتبر مثالًا قياسيًا للعملة المشفرة. برزت عملة البيتکوين باعتبارها العملة الافتراضية الأکثر شعبية ، وتحتفظ بأکبر حصة في المستقبل من منافسيها) الايثيرم ، الريبل ، اليتکوين والبيتکوين کاش).
Graph convolutional networks (GCNs) have been employed as a kind of significant tool on many graph-based applications recently. Inspired by convolutional neural networks (CNNs), GCNs generate the embeddings of nodes by aggregating the information of their neighbors layer by layer. However, the high computational and memory cost of GCNs due to the recursive neighborhood expansion across GCN layers makes it infeasible for training on large graphs. To tackle this issue, several sampling methods during the process of information aggregation have been proposed to train GCNs in a mini-batch Stochastic Gradient Descent (SGD) manner. Nevertheless, these sampling strategies sometimes bring concerns about insufficient information collection, which may hinder the learning performance in terms of accuracy and convergence. To tackle the dilemma between accuracy and efficiency, we propose to use aggregators with different granularities to gather neighborhood information in different layers. Then, a degree-based sampling strategy, which avoids the exponential complexity, is constructed for sampling a fixed number of nodes. Combining the above two mechanisms, the proposed model, named Mix-grained GCN (MG-GCN) achieves state-of-the-art performance in terms of accuracy, training speed, convergence speed, and memory cost through a comprehensive set of experiments on four commonly used benchmark datasets and a new Ethereum dataset.
Sina Rafati Niya, Benjamin Jeffrey, Burkhard Stiller
The integration of Internet-of-Things (IoT) and Blockchains (BC) for trusted and decentralized approaches enabled modern use cases, such as supply chain tracing, smart cities, and IoT data marketplaces. For these it is essential to identify reliably IoT devices, since the producer-consumer trust is not guaranteed by a Trusted Third Party (TTP). Therefore, this work proposes a Know Your IoT device platform (KYoT), which enables the self-sovereign identification of IoT devices on the Ethereum BC. KYoT permits manufacturers and device owners to register and verify IoT devices in a self-sovereign fashion, while data storage security is ensured. KYoT deploys an SRAM-based (Static Random Access Memory) Physically Unclonable Function (PUF), which takes advantage of the manufacturing variability of devices' SRAM chips to derive a unique identifying key for each IoT device. The self-sovereign identification mechanism introduced is based on the ERC 734 and ERC 735 Ethereum identity standards.
Open access
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Physical Unclonable Functions (PUFs) and Hardware Security
Cryptocurrencies, which the Bitcoin is the most remarkable one, have allured substantial awareness up to now, and they have encountered enormous instability in their price. While some studies utilize conventional statistical and econometric ways to uncover the driving variables of Bitcoin's prices, experimentation on the advancement of predicting models to be used as decision support tools in investment techniques is rare. There are many different predicting cryptocurrencies' price methods that cover various purposes, such as forecasting a one-step approach that can be done through time series analysis, neural networks, and machine learning algorithms. Sometimes realizing the trend of a coin in a long run period is needed. In this paper, some machine learning algorithms are applied to find the best ones that can forecast Bitcoin price based on three other famous coins. Second, a new methodology is developed to predict Bitcoin's worth, this is also done by considering different cryptocurrencies prices (Ethereum, Zcash, and Litecoin). The results demonstrated that Zcash has the best performance in forecasting Bitcoin's price without any data on Bitcoin's fluctuations price among these three cryptocurrencies.
Harry Cai, Daniel Rueckert, Jonathan Passerat-Palmbach
Federated Learning harnesses data from multiple sources to build a single model. While the initial model might belong solely to the actor bringing it to the network for training, determining the ownership of the trained model resulting from Federated Learning remains an open question. In this paper we explore how Blockchains (in particular Ethereum) can be used to determine the evolving ownership of a model trained with Federated Learning. Firstly, we use the step-by-step evaluation metric to assess the relative contributivities of participants in a Federated Learning process. Next, we introduce 2CP, a framework comprising two novel protocols for Blockchained Federated Learning, which both reward contributors with shares in the final model based on their relative contributivity. The Crowdsource Protocol allows an actor to bring a model forward for training, and use their own data to evaluate the contributions made to it. Potential trainers are guaranteed a fair share of the resulting model, even in a trustless setting. The Consortium Protocol gives trainers the same guarantee even when no party owns the initial model and no evaluator is available. We conduct experiments with the MNIST dataset that reveal sound contributivity scores resulting from both Protocols by rewarding larger datasets with greater shares in the model. Our experiments also showed the necessity to pair 2CP with a robust model aggregation mechanism to discard low quality inputs coming from model poisoning attacks.
Many empirical software engineering studies show that there is a need for repositories where source codes are acquired, filtered and classified. During the last few years, Ethereum block explorer services have emerged as a popular project to explore and search for Ethereum blockchain data such as transactions, addresses, tokens, smart contracts’ source codes, prices and other activities taking place on the Ethereum blockchain. Despite the availability of this kind of service, retrieving specific information useful to empirical software engineering studies, such as the study of smart contracts’ software metrics, might require many subtasks, such as searching for specific transactions in a block, parsing files in HTML format, and filtering the smart contracts to remove duplicated code or unused smart contracts. In this paper, we afford this problem by creating Smart Corpus, a corpus of smart contracts in an organized, reasoned and up-to-date repository where Solidity source code and other metadata about Ethereum smart contracts can easily and systematically be retrieved. We present Smart Corpus’s design and its initial implementation, and we show how the data set of smart contracts’ source codes in a variety of programming languages can be queried and processed to get useful information on smart contracts and their software metrics. Smart Corpus aims to create a smart-contract repository where smart-contract data (source code, application binary interface (ABI) and byte code) are freely and immediately available and are classified based on the main software metrics identified in the scientific literature. Smart contracts’ source codes have been validated by EtherScan, and each contract comes with its own associated software metrics as computed by the freely available software PASO. Moreover, Smart Corpus can be easily extended as the number of new smart contracts increases day by day.
Abstract We study the fundamental differences that separate: Litecoin; Bitcoin Gold; Bitcoin Cash; Ethereum; and Zcash from Bitcoin, and draw some analysis to how these features are appreciated by the market, to ultimately make an inference as to how future successful cryptocurrencies may be invented and behave. We use Google Trend data, as well as price, volume and market capitalization data sourced from coinmarketcap.com to support this analysis. We find that Litecoin’s shorter block times offer benefits in commerce, but drawbacks in the mining process through orphaned blocks. Zcash holds a niche use for anonymous transactions, benefitting areas of the world lacking in economic freedom. Bitcoin Cash suffers from centralization in the mining process, while the greater decentralization of Bitcoin Gold has generally left it to stagnate. Ether’s greater functionality offers the greatest threat to Bitcoin’s dominance in the market. A coin that incorporates several of these features can be technically better than Bitcoin, but the first-to-market advantage of Bitcoin should keep its dominant position in the market.
Callbacks are an effective programming discipline for implementing event-driven programming, especially in environments like Ethereum which forbid shared global state and concurrency. Callbacks allow a callee to delegate the execution back to the caller. Though effective, they can lead to subtle mistakes principally in open environments where callbacks can be added in a new code. Indeed, several high profile bugs in smart contracts exploit callbacks. We present the first static technique ensuring modularity in the presence of callbacks and apply it to verify prominent smart contracts. Modularity ensures that external calls to other contracts cannot affect the behavior of the contract. Importantly, modularity is guaranteed without restricting programming. In general, checking modularity is undecidable—even for programs without loops. This paper describes an effective technique for soundly ensuring modularity harnessing SMT solvers. The main idea is to define a constructive version of modularity using commutativity and projection operations on program segments. We believe that this approach is also accessible to programmers, since counterexamples to modularity can be generated automatically by the SMT solvers, allowing programmers to understand and fix the error. We implemented our approach in order to demonstrate the precision of the modularity analysis and applied it to real smart contracts, including a subset of the 150 most active contracts in Ethereum. Our implementation decompiles bytecode programs into an intermediate representation and then implements the modularity checking using SMT queries. Overall, we argue that our experimental results indicate that the method can be applied to many realistic contracts, and that it is able to prove modularity where other methods fail.
Static analysis of smart contracts as-deployed on the Ethereum blockchain has received much recent attention. However, high-precision analyses currently face significant challenges when dealing with the Ethereum VM (EVM) execution model. A major such challenge is the modeling of low-level, transient “memory” (as opposed to persistent, on-blockchain “storage”) that smart contracts employ. Statically understanding the usage patterns of memory is non-trivial, due to the dynamic allocation nature of in-memory buffers. We offer an analysis that models EVM memory, recovering high-level concepts (e.g., arrays, buffers, call arguments) via deep modeling of the flow of values. Our analysis opens the door to Ethereum static analyses with drastically increased precision. One such analysis detects the extraction of ERC20 tokens by unauthorized users. For another practical vulnerability (redundant calls, possibly used as an attack vector), our memory modeling yields analysis precision of 89%, compared to 16% for a state-of-the-art tool without precise memory modeling. Additionally, precise memory modeling enables the static computation of a contract’s gas cost. This gas-cost analysis has recently been instrumental in the evaluation of the impact of the EIP-1884 repricing (in terms of gas costs) of EVM operations, leading to a reward and significant publicity from the Ethereum Foundation.
Ilhaam A. Omar, Mazin Debe, Raja Jayaraman, Khaled Salah · 6 authors
<div>The COVID-19 pandemic has severely impacted many industries, in particular the healthcare sector exposing systemic vulnerabilities in emergency preparedness, risk mitigation, and supply chain management. A major challenge during the pandemic was related to the increased demand of Personal Protective Equipment (PPE) resulting in critical shortages for healthcare and frontline workers. The lack of information visibility combined with the inability to precisely track product movement within the supply chain requires an robust traceability solution. Blockchain technology is a distributed ledger that ensures a transparent,</div><div>safe, and secure exchange of data among supply chain stakeholders. The advantages of adopting blockchain technology to manage and track PPE products in the supply chain include decentralized control, security, traceability,</div><div>and auditable time-stamped transactions. In this paper, we present a blockchain-based approach using smart contracts to transform PPE supply chain operations. We propose a generic framework using Ethereum smart contracts and</div><div>decentralized storage systems to automate the processes and information exchange and present detailed algorithms that capture the interactions among supply chain stakeholders. The smart contract code was developed and tested in Remix environment, and the code is made publicly available on Github. We present detailed cost and security analysis incurred by the stakeholders in the supply chain. Adopting a blockchain-based solution for PPE supply chains is economically viable and provides a streamlined, secure, trusted, and transparent mode of communication among various stakeholders.</div>
ML-as-a-service (MLaaS) becomes increasingly popular and revolutionizes the lives of people. A natural requirement for MLaaS is, however, to provide highly accurate prediction services. To achieve this, current MLaaS systems integrate and combine multiple well-trained models in their services. Yet, in reality, there is no easy way for MLaaS providers, especially for startups, to collect sufficiently well-trained models from individual developers, due to the lack of incentives. In this article, we aim to fill this gap by building up a model marketplace, called as Golden Grain, to facilitate model sharing, which enforces the fair model-money swapping process between individual developers and MLaaS providers. Specifically, we deploy the swapping process on the blockchain, and further introduce a blockchain-empowered model benchmarking process for transparently determining the model prices according to their authentic performances, so as to motivate the faithful contributions of well-trained models. Especially, to ease the blockchain overhead for model benchmarking, our marketplace carefully offloads the heavy computation and designs a secure off-chain on-chain interaction protocol based on a trusted execution environment (TEE), for ensuring both the integrity and authenticity of benchmarking. We implement a prototype of our Golden Grain on the Ethereum blockchain, and conduct extensive experiments using standard benchmark datasets to demonstrate the practically affordable performance of our design.
P. Sheela Rani, Sankara Revathi S, J S Dharshini, M Rekha
The Internet of Things (IOT) is integrated with supply chain management process to track the product. To track the product smart tags is used. The smart tags like QR code and NFC is used. But with the technology enhancement the block chain is introduced into the supply chain management process. The block chain is the great revolution that data in the centralized form is transformed in to a decentralized manner. The distributed Ledger Technology (DLT) is one of the method used in ethereum block chain. The main advantage of using DLT is, it offers decentralized, privacy-preserving and verifiable process in the smart tags. In existing system only single server was used to maintain all the process like supplier, manufacturer and distributor. In this application we are using different server which was more secure than existing system. The proposed solution in this paper is it checks the product evidence during the entire lifecycle of the product by using the smart contract. The data can be immutable by using smart contract with ethereum block chain. The duplication is manipulated by the block chainserver.
Abstract Through the application of the VAR-AGARCH model to intra-day data for three cryptocurrencies (Bitcoin, Ethereum, and Litecoin), this study examines the return and volatility spillover between these cryptocurrencies during the pre-COVID-19 period and the COVID-19 period. We also estimate the optimal weights, hedge ratios, and hedging effectiveness during both sample periods. We find that the return spillovers vary across the two periods for the Bitcoin-Ethereum, Bitcoin-Litecoin, and Ethereum-Litecoin pairs. However, the volatility transmissions are found to be different during the two sample periods for the Bitcoin-Ethereum and Bitcoin-Litecoin pairs. The constant conditional correlations between all pairs of cryptocurrencies are observed to be higher during the COVID-19 period compared to the pre-COVID-19 period. Based on optimal weights, investors are advised to decrease their investments (a) in Bitcoin for the portfolios of Bitcoin/Ethereum and Bitcoin/Litecoin and (b) in Ethereum for the portfolios of Ethereum/Litecoin during the COVID-19 period. All hedge ratios are found to be higher during the COVID-19 period, implying a higher hedging cost compared to the pre-COVID-19 period. Last, the hedging effectiveness is higher during the COVID-19 period compared to the pre-COVID-19 period. Overall, these findings provide useful information to portfolio managers and policymakers regarding portfolio diversification, hedging, forecasting, and risk management.
Summary The “last mile” problem in logistics is challenging due to its low efficiency and high cost. To address this problem, Unmanned Aerial Vehicle (UAV) delivery such as drone delivery has been proposed and widely accepted as a promising solution. However, currently most of the existing UAV delivery systems are based on Cloud Computing which cannot efficiently meet the requirements of many real‐time services in UAV delivery systems. Meanwhile, the security issues in UAV delivery systems also raise critical concerns due to the existence of multiple participants (such as the sender, middler, and receiver) who may not maintain a mutual trust relationship among them. How to secure the UAV delivery process in such an untrusted environment is still a challenging issue. In this paper, we propose a Mobile Edge Computing (MEC) and blockchain‐based UAV delivery system to resolve the “last mile” problem in logistics. Specifically, based on the MEC architecture, the blockchain nodes are deployed on the edge nodes to facilitate and secure the UAV delivery process. To verify the effectiveness of our proposed solution, a MEC‐based UAV delivery system prototype with a private blockchain on the Ethereum platform is implemented. Through the security analysis and performance evaluation, it is proven that our proposed solution can effectively solve the “last mile” problem and address the security issues in UAV delivery systems.
This study will investigate the predictability of a Chaotic time-series data using Reservoir computing (Echo State Network), Deep-Learning(LSTM) and Machine- Learning(Linear, Bayesian, ElasticNetCV , Random Forest, XGBoost Regression and a machine learning Neural Network) on the short (1-day out prediction), medium (5-day out prediction) and long-term (30-day out prediction) pricing of Bitcoin and Ethereum Using a range of machine learning tools, to perform feature selection by permutation importance to select technical indicators on the individual cryptocurrencies, to ensure the datasets are the best for predictions per cryptocurrency while reducing noise within the models. The predictability of these two chaotic time-series is then compared to evaluate the models to find the best fit model. The models are fine-tuned, with hyperparameters, design of the network within the LSTM and the reservoir size within the Echo State Network being adjusted to improve accuracy and speed. This research highlights the effect of the trends within the cryptocurrency and its effect on predictive models, these models will then be optimized with hyperparameter tuning, and be evaluated to compare the models across the two currencies. It is found that the datasets for each cryptocurrency are different, due to the different permutation importance, which does not affect the overall predictability of the models with the short and medium-term predictions having the same models being the top performers. This research confirms that the chaotic data although can have positive results for shortand medium-term prediction, for long-term prediction, technical analysis basedprediction is not sufficient.
The production of counterfeit money has a long history. It refers to the creation of imitation currency that is produced without the legal sanction of government. With the growth of the cryptocurrency ecosystem, there is expanding evidence that counterfeit cryptocurrency has also appeared. In this paper, we empirically explore the presence of counterfeit cryptocurrencies on Ethereum and measure their impact. By analyzing over 190K ERC-20 tokens (or cryptocurrencies) on Ethereum, we have identified $2,117$ counterfeit tokens that target 94 of the 100 most popular cryptocurrencies. We perform an end-to-end characterization of the counterfeit token ecosystem, including their popularity, creators and holders, fraudulent behaviors and advertising channels. Through this, we have identified two types of scams related to counterfeit tokens and devised techniques to identify such scams. We observe that over 7,104 victims were deceived in these scams, and the overall financial loss sums to a minimum of \$ 17 million (74,271.7 ETH). Our findings demonstrate the urgency to identify counterfeit cryptocurrencies and mitigate this threat.
Giuseppe Antonio Pierro, Roberto Tonelli, Michele Marchesi
Many empirical software engineering studies show that there is a great need for repositories where source code is acquired, filtered and classified. During the last few years, Ethereum block explorer services have emerged as a popular project to explore and search Ethereum blockchain data such as transactions, addresses, tokens, smart-contracts' source code, prices and other activities taking place on the Ethereum blockchain. Despite the availability of this kind of services, retrieving specific information useful to empirical software engineering studies, such as the study of smart-contracts' software metrics might require many sub-tasks, such as searching specific transactions in a block, parsing files in HTML format and filtering the smart-contracts to remove duplicated code or unused smart-contracts. In this paper we afford this problem creating Smart Corpus', a Corpus of Smart Contracts in an organized reasoned and up to date repository where Solidity source code and other metadata about Ethereum smart contracts can easily and systematically be retrieved. We present the Smart Corpus' design and its initial implementation and we show how the data-set of smart contracts' source code in a variety of programming languages can be queried and processed, get useful information on smart contracts and their software metrics. The Smart Corpus aims to create a smart-contracts' repository where smart contracts data (source code, ABI and byte-code) are freely and immediately available and also classified based on the main software metrics identified in the scientific literature. Smart-contracts source code has been validated by EtherScan and each contract comes with its own associated software metrics as computed by the freely available software PASO. Moreover, Smart Corpus can be easily extended, as the number of new smart-contracts increases day by day.
Received wisdom portrays digital records as guaranteeing perpetuity; as the New York Times wrote a decade ago: "the web means the end of forgetting". The reality however is that digital records suffer similar risks of access loss as the analogue versions they replace. Often this risk is outsourced to specialised third parties. Common use cases include Personal Information Management (PIM): e.g. calendars, diaries, tasks, etc. Frequently these are outsourced at two removes - firstly by the individual to their employer (e.g. using a company system) and then by their employer to an external provider. So enters a new risk: organisational change; by the time the information is required the organisational chain that links user to data may be broken: the employer transitions to a different provider, the employee leaves the company, the IS provider pivots to new offerings. The advent of Distributed Ledger Technology (DLT) could help mitigate these risks; and has led to a re-evaluation of the relationship between data creation and ownership. Although DLT is an imprecise term, it typically involves data storage across organisationally separate entities in a cryptographically secure form; and therefore could present a partial solution to the risk. This project presents the first research that applies DLT to the field of PIM, furthering design science state of the art by a novel implementation of a calendar application on the Ethereum blockchain. It also extends current research in utilising DLT in digital preservation, namely by enacting a continuum approach within a DL that allows for transfer of ownership of digital objects as they transition from individual to collective relevance. Finally it provides guidelines for future use of DLT within digital preservation.
We propose Agora, the first privacy-aware data marketplace that enables parties to get compensated for contributing data, without relying on a trusted third party. We leverage cryptographic techniques to achieve three security properties: (i) data privacy-raw data remain private except for a function output, (ii) output verifiability-the output is proven to be correct, and (iii) atomicity of payments-parties cannot avoid paying for provided services. Agora is designed as a decentralized blockchain application via smart contracts. We implement a prototype on Ethereum and evaluate its performance in terms of computation overhead and monetary cost.
In this study, a novel blockchain-based platform for renewable energy investment is proposed. The blockchain technology has been shown to be a reliable way to send financial transactions across the world in a decentralized manner, at a fraction of the cost and with a faster transaction time. The proposed platform is capable of handling an equity-sharing investment program for solar PV projects. The platform distributes the electricity generation income by sending it over a blockchain. The Ethereum blockchain is used to create a prototype, which is a smart contract. The prototype is tested with a solar-PV electricity generation data. The result shows that the proposed platform can handle the expected transactions without an error. The overall performance of the proposed platform is tested by comparing its temporal duration and its financial costs with centralized solutions. Therefore, the proposed platform provides all the benefits mentioned at a much cheaper cost.