Follow blockchain research across journals, conferences, and preprint repositories.
13,620 results · page 480 of 568
Nan Jia, Queping Kong, Haiping Huang
No abstract is available for this record.
Mohamed Laarabi, Abdelilah Maach
No abstract is available for this record.
Anton Wahrstätter
No abstract is available for this record.
Sandeep Kadam, Shajimon K. John
No abstract is available for this record.
Aboua Ange Kevin N’DA, Santiago Matalonga, Keshav Dahal
No abstract is available for this record.
Konstantinos Tsoulias, Georgios Palaiokrassas, Georgios Fragkos, Αντώνιος Λίτκε · 5 authors
Blockchains are being recently used as a supporting technology framework for decentralized applications requiring functionalities such as exchange of value through tokens, cryptocurrency and smart contracts. In this paper, we have developed a decentralized application model in Python, where blockchain data are stored in a Neo4j graph database. Following the basic principles of Ethereum blockchain network, we implemented a Casper-like consensus mechanism and tested its effectiveness in achieving finality. For block proposing, we employed both Proof of Work and Proof of Stake protocols and examined how participants' incentives and consensus criteria differ according to each one. A major part of this work is to incorporate the graph model in the functionality of the blockchain and its components, while also exploiting its benefits in data analysis by finding relationships between data and extracting their true value. Through this approach, we were able to monitor and visualize changes in blockchain data in various use case scenarios. Lastly, we ran a series of simulated experiments to test the efficiency of the implemented technologies and mechanisms in preventing the most common blockchain attacks such as the 51% Attack, Catastrophic Crashes and Attack from dynamic validator sets. We show how the modelling of the blockchain data as a distributed graph can assist protocols operations, enhance their security, and facilitate the application of analytical methods to the stored information through path-dependent queries.
Vikas Jaiman, Visara Urovi
In modern healthcare systems, the ability to share electronic health records is crucial for providing quality care and for enabling a larger spectrum of health services. Health data sharing is dependent on obtaining individual consent which, in turn, is hindered by a lack of resources. To this extent, blockchain-based platforms facilitate data sharing by creating a trusted distributed network of users. These users are enabled to share their data without depending on the time and resources of specific actors (such as the health services). In blockchain-based platforms, data governance mechanisms become very important due to the need to specify and monitor data sharing and data use conditions. In this article, we present a blockchain-based data-sharing consent model for access control over individual health data. We use smart contracts to dynamically represent the individual’s consent over health data and to enable data requesters to search and access those data. The dynamic consent model extends to two ontologies: the Data Use Ontology (DUO) which models the individual consent of users and the Automatable Discovery and Access Matrix (ADA-M), which describes queries from data requesters. We deploy the model on Ethereum blockchain and evaluate different data sharing scenarios. The contribution of this article consists of the creation of an individual consent model for health data sharing platforms. Such a model guarantees that individual consent is respected and that all the participants in the data sharing platform are accountable. The evaluation of our solution indicates that such a data sharing model provides a flexible decision-making approach over data usage by data requesters. Our experimental evaluation shows that the proposed model is efficient and adapts to personalized access control policies in different data-sharing scenarios.
Raifa Akkaoui, Xiaojun Hei, Wenqing Cheng
Recently, researchers around the world in medical institutions and pharmaceutical companies are demanding a wider access to healthcare data for secondary use in order to provide enhanced and personalized medical services. For this purpose, healthcare information exchange between health authorities can be leveraged as a fundamental concept to meet these demands and enable the discovery of new insights and cures. However, health data are highly sensitive and private information that requires strong authentication and authorization procedures to manage the access to them. In this regard, the cloud paradigm has been used in these e-healthcare solutions, but they remain inefficient due to their inability to adapt to the expanding volume of data generated from body sensors and their vulnerability against cyberattacks. Hence, collaborative and distributed data governance supported by edge computing and blockchain promises enormous potentials in improving the performance and security of the whole system. In this paper, we present a secure and efficient data management framework, named ”EdgeMediChain”, for sharing health data. The proposed architecture leverages both edge computing and blockchain to facilitate and provide the necessary requirements for a healthcare ecosystem in terms of scalability, security, as well as privacy. The Ethereum-based testbed evaluations show the effectiveness of EdgeMediChain in terms of execution time with a reduction of nearly 84.75% for 2000 concurrent transactions, higher throughput compared to a traditional blockchain, and scalable ledger storage with a linear growth rate.
Selin Sezer, Clemens Eyhoff, Wolfgang Prinz, Thomas Rose
Due to the increase in smart contracts in Ethereum, a need for proper classification has emerged. Although the smart contracts are accessible due to the open nature of the Blockchain, readability is still an issue with respect to the smart contract bytecode. We propose an automated approach for classifying smart contracts that utilize popular text classification methods on the opcode translation of the smart contract bytecode in order to overcome this limitation. Our experiments indicate that the decision-tree-based techniques like Random Forest and Xgboost outmatch the traditional classification tools like Naïve Bayes, Logistic Regression, and SVM once the opcode input is presented as n-gram tf-idf vectors.
Clara Schneidewind, Markus Scherer, Matteo Maffei
Ethereum smart contracts are distributed programs running on top of the Ethereum blockchain. Since program flaws can cause significant monetary losses and can hardly be fixed due to the immutable nature of the blockchain, there is a strong need of automated analysis tools which provide formal security guarantees. Designing such analyzers, however, proved to be challenging and error-prone. We review the existing approaches to automated, sound, static analysis of Ethereum smart contracts and highlight prevalent issues in the state of the art. Finally, we overview eThor, a recent static analysis tool that we developed following a principled design and implementation approach based on rigorous semantic foundations to overcome the problems of past works.
Marco Bareis, Monika di Angelo, Gernot Salzer
No abstract is available for this record.
Muhammad ElSheikh, Jeremy Clark, Amr Youssef
No abstract is available for this record.
Lars Brünjes, Murdoch J. Gabbay
We implement two versions of a simple but illustrative smart contract: one in Solidity on the Ethereum blockchain platform, and one in Plutus on the Cardano platform, with annotated code excerpts and with source code attached. We get a clearer view of the Cardano programming model in particular by introducing a novel mathematical abstraction which we call Idealised EUTxO. For each version of the contract, we trace how the architectures of the underlying platforms and their mathematics affects the natural programming styles and natural classes of errors. We prove some simple but novel results about alpha-conversion and observational equivalence for Cardano, and explain why Ethereum does not have them. We conclude with a wide-ranging and detailed discussion in the light of the examples, mathematical model, and mathematical results so far.
Imane Mokdad, Nabil M. Hewahi
No abstract is available for this record.
Zeinab Nehaï, François Bobot
No abstract is available for this record.
Elvira Albert, Pablo Gordillo, Albert Rubio, Maria A. Schett
With the advent of smart contracts that execute on the blockchain ecosystem, a new mode of reasoning is required for developers that must pay meticulous attention to the gas spent by their smart contracts, as well as for optimization tools that must be capable of effectively reducing the gas required by the smart contracts. Super-optimization is a technique which attempts to find the best translation of a block of code by trying all possible sequences of instructions that produce the same result. This paper presents a novel approach for super-optimization of smart contracts based on Max-SMT which is split into two main phases: (i) the extraction of a stack functional specification from the basic blocks of the smart contract, which is simplified using rules that capture the semantics of the arithmetic, bit-wise, relational operations, etc. (ii) the synthesis of optimized blocks which, by means of an efficient Max-SMT encoding, finds the bytecode blocks with minimal gas cost whose stack functional specification is equal (modulo commutativity) to the extracted one. Our experimental results are very promising: we are able to optimize 55.41 % of the blocks, and prove that 34.28 % were already optimal, for more than 61000 blocks from the most called 2500 Ethereum contracts.
Xiaoqi Li, Ting Chen, Xiapu Luo, Tao Zhang · 6 authors
More than eight million smart contracts have been deployed into Ethereum, which is the most popular blockchain that supports smart contract. However, less than 1% of deployed smart contracts are open-source, and it is difficult for users to understand the functionality and internal mechanism of those closed-source contracts. Although a few decompilers for smart contracts have been recently proposed, it is still not easy for users to grasp the semantic information of the contract, not to mention the potential misleading due to decompilation errors. In this paper, we propose the first system named STAN to generate descriptions for the bytecodes of smart contracts to help users comprehend them. In particular, for each interface in a smart contract, STAN can generate four categories of descriptions, including functionality description, usage description, behavior description, and payment description, by leveraging symbolic execution and NLP (Natural Language Processing) techniques. Extensive experiments show that STAN can generate adequate, accurate, and readable descriptions for contract's bytecodes, which have practical value for users.
Auqib Hamid Lone, Roohie Naaz Mir
No abstract is available for this record.
Pavel V. Sukharev
Mining task is remarkable by the high amount of resources, such as electrical power, which is needed for its execution. In order to reduce the amount of resources consumed, while maintaining high mining performance, the term of mining efficiency was introduced. This term considering both mining performance and consumed electrical power.In this paper we consider mining hardware overclocking in order to maximize mining efficiency. Overclocking methods are considered, such as increasing memory clock rate, decreasing core clock rate, decreasing hardware voltage. A complex technique is created that allows to apply the considered approaches in order to achieve maximum mining efficiency for a hardware system being overclocked.
Asena Deniz, Dilek Teker
One of the most important innovations brought by digitalization is crypto money known as virtual money. Cryptocurrencies, which have been discussed in recent years and especially a new portfolio for investors, are very popular. Bitcoin is the most well-known of these cryptographic systems, which do not depend on a central authority and have maximum reliability. The effects of various financial indicators on cryptoparas were examined in this study. The model includes a daily database in between April 3, 2018 to December 31, 2019. Initially stationarity is tested with unit root tests. Then cointegration and causality tests are employed. Impulse response is also implemented and analysed.
Jonathan H. Clark
Distributed ledger technology (DLT) stands to benefit industries such as financial services with transparency and censorship resistance. DLT systems need to be scalable to handle mass user adoption. Mass user adoption is required to demonstrate the true value of DLT. This dissertation first analyses scalability in ethereum and EOS. Currently, ethereum 1.0 uses proof of work (PoW) and handles only 14 transactions per second (tps) compared to Visa's peak 47 000 tps. Ethereum 2.0, known as Serenity, introduces sharding, proof of stake (Casper), plasma and state channels in and effort to scale the system. EOS uses a delegated proof of stake (DPoS) protocol, where 21 super-nodes, termed ‘block producers' (BPs), facilitate consensus, bringing about significant scalability improvements (4000 tps). The trade-off is decentralisation. EOS is not sufficiently decentralised because the BPs yield significant power, but are not diverse. This dissertation conducts an empirical analysis using unsupervised machine learning to show that there is a high probability collusion is occurring between certain BPs. It then suggests possible protocol alterations such as inverse vote weighting that could curb adverse voting behaviour in DPoS. It further analyses whether universities are suitable BP's before mapping out required steps for universities to become block producers (leading to improved decentralisation in EOS)
Mohammad Madine, Ammar Battah, Ibrar Yaqoob, Khaled Salah · 8 authors
Personal health records (PHRs) are valuable assets to individuals because they enable them to integrate and manage their medical data. A PHR is an electronic application through which patients can manage their health information. Giving patients control over their medical data offers an advantageous realignment of the doctor-patient dynamic. However, today's PHR management systems fall short of giving reliable, traceable, trustful, and secure patients control over their medical data, which poses serious threats to their authenticity and accuracy. Moreover, most of the current approaches and systems leveraged for managing PHR are centralized that not only make medical data sharing difficult but also poses a risk of single point of failure problem. In this paper, we propose Ethereum blockchain-based smart contracts to give patients control over their data in a manner that is decentralized, immutable, transparent, traceable, trustful, and secure. The proposed system employs decentralized storage of interplanetary file systems (IPFS), proxy re-encryption, and trusted reputation-based oracles to securely fetch, store, and share patients' medical data. We present algorithms along with their full implementation details. We evaluate the proposed smart contracts using two important performance metrics, such as cost and correctness. Furthermore, we provide security analysis and discuss the generalization aspects of our solution. We outline the limitations of the proposed approach. We make the smart contract source code publicly available on Github.
Javier Arcenegui, Rosario Arjona, Iluminada Baturone
No abstract is available for this record.
Ahmad Sghaier Omar, Otman Basir
No abstract is available for this record.