Rob van Glabbeek, Vincent Gramoli, Pierre Tholoniat
In this paper, we consider the problem of cross-chain payment whereby customers of different escrows -- implemented by a bank or a blockchain smart contract -- successfully transfer digital assets without trusting each other. Prior to this work, cross-chain payment problems did not require this success or any form of progress. We introduce a new specification formalism called Asynchronous Networks of Timed Automata (ANTA) to formalise such protocols. We present the first cross-chain payment protocol that ensures termination in a bounded amount of time and works correctly in the presence of clock skew. We then demonstrate that it is impossible to solve this problem without assuming synchrony, in the sense that each message is guaranteed to arrive within a known amount of time. We also offer a protocol that solves an eventually terminating variant of this cross-chain payment problem without synchrony, and even in the presence of Byzantine failures.
Jiaming Ye, Mingliang Ma, Yun Lin, Lei Ma · 6 authors
Recent years have seen smart contracts are getting increasingly popular in building trustworthy decentralized applications. Previous research has proposed static and dynamic techniques to detect vulnerabilities in smart contracts. These tools check vulnerable contracts against several predefined rules. However, the emerging new vulnerable types and programming skills to prevent possible vulnerabilities emerging lead to a large number of false positive and false negative reports of tools. To address this, we propose Vulpedia, which mines expressive vulnerability signatures from contracts. Vulpedia is based on the relaxed assumption that the owner of contract is not malicious. Specifically, we extract structural program features from vulnerable and benign contracts as vulnerability signatures, and construct a systematic detection method based on detection rules composed of vulnerability signatures. Compared with the rules defined by state-of-the-arts, our approach can extract more expressive rules to achieve better completeness (i.e., detection recall) and soundness (i.e., precision). We further evaluate Vulpedia with four baselines (i.e., Slither, Securify, SmartCheck and Oyente) on the testing dataset consisting of 17,770 contracts. The experiment results show that Vulpedia achieves best performance of precision on 4 types of vulnerabilities and leading recall on 3 types of vulnerabilities meanwhile exhibiting the great efficiency performance.
Quality of clinical research is undermined by severe misconducts, errors, frauds, which are detrimental to the trust it should arouse. In this perspective article, we show how Blockchain may trace and control processes of Clinical Trials preventing from the above issues or at least discourage them since they would become traceable and opposable. Then, we propose a short and doable program where, amidst the complex stream of events that a Clinical Trials consist of, we select sensitive and misconduct-prone steps that could dramatically benefit from Blockchain, through either simple core features as traceability and incorruptibility of its data registration, or through more refined automation tools called Smart Contracts.
Open access
Blockchain Technology Applications and Security
Ethics in Clinical Research
Artificial Intelligence in Healthcare and Education
This paper analyses how blockchain technology works in agri-food supply chains. It also intends to identify the benefits and limitations associated with the potential widespread use of this technology to improve efficiency and sustainability performance. Despite the undeniable benefits of blockchain and the possible improvements to enhance transparency, reliability and accuracy of information, considerable challenges to its widespread adoption persist. The transformational potential of blockchain technology can be unlocked by: a) the creation of a pre-competitive space for unprecedented horizontal collaboration amongst supply chain actors to reduce cost and maximise benefits; b) the redesign of centralised governance systems of existing tracking and information-sharing platforms; c) the integration of blockchain and Internet of Things devices.
Programming language design requires making many usability-related design decisions. However, existing HCI methods can be impractical to apply to programming languages: they have high iteration costs, programmers require significant learning time, and user performance has high variance. To address these problems, we adapted both formative and summative HCI methods to make them more suitable for programming language design. We integrated these methods into a new process, PLIERS, for designing programming languages in a user-centered way. We evaluated PLIERS by using it to design two new programming languages. Glacier extends Java to enable programmers to express immutability properties effectively and easily. Obsidian is a language for blockchains that includes verification of critical safety properties. Summative usability studies showed that programmers were able to program effectively in both languages after short training periods.
For the modern world where data is becoming one of the most valuable assets,\nrobust data privacy policies rooted in the fundamental infrastructure of\nnetworks and applications are becoming an even bigger necessity to secure\nsensitive user data. In due course with the ever-evolving nature of newer\nstatistical techniques infringing user privacy, machine learning models with\nalgorithms built with respect for user privacy can offer a dynamically adaptive\nsolution to preserve user privacy against the exponentially increasing\nmultidimensional relationships that datasets create. Using these privacy aware\nML Models at the core of a Federated Learning Ecosystem can enable the entire\nnetwork to learn from data in a decentralized manner. By harnessing the\never-increasing computational power of mobile devices, increasing network\nreliability and IoT devices revolutionizing the smart devices industry, and\ncombining it with a secure and scalable, global learning session backed by a\nblockchain network with the ability to ensure on-device privacy, we allow any\nInternet enabled device to participate and contribute data to a global privacy\npreserving, data sharing network with blockchain technology even allowing the\nnetwork to reward quality work. This network architecture can also be built on\ntop of existing blockchain networks like Ethereum and Hyperledger, this lets\neven small startups build enterprise ready decentralized solutions allowing\nanyone to learn from data across different departments of a company, all the\nway to thousands of devices participating in a global synchronized learning\nnetwork.\n
Muqaddas Naz, Fahad Ahmed Al-Zahrani, Rabiya Khalid, Nadeem Javaid · 7 authors
In a research community, data sharing is an essential step to gain maximum knowledge from the prior work. Existing data sharing platforms depend on trusted third party (TTP). Due to the involvement of TTP, such systems lack trust, transparency, security, and immutability. To overcome these issues, this paper proposed a blockchain-based secure data sharing platform by leveraging the benefits of interplanetary file system (IPFS). A meta data is uploaded to IPFS server by owner and then divided into n secret shares. The proposed scheme achieves security and access control by executing the access roles written in smart contract by owner. Users are first authenticated through RSA signatures and then submit the requested amount as a price of digital content. After the successful delivery of data, the user is encouraged to register the reviews about data. These reviews are validated through Watson analyzer to filter out the fake reviews. The customers registering valid reviews are given incentives. In this way, maximum reviews are submitted against every file. In this scenario, decentralized storage, Ethereum blockchain, encryption, and incentive mechanism are combined. To implement the proposed scenario, smart contracts are written in solidity and deployed on local Ethereum test network. The proposed scheme achieves transparency, security, access control, authenticity of owner, and quality of data. In simulation results, an analysis is performed on gas consumption and actual cost required in terms of USD, so that a good price estimate can be done while deploying the implemented scenario in real set-up. Moreover, computational time for different encryption schemes are plotted to represent the performance of implemented scheme, which is shamir secret sharing (SSS). Results show that SSS shows the least computational time as compared to advanced encryption standard (AES) 128 and 256.
The current standard in accounting practice is the double-entry approach. Basis of the double-entry approach is that every financial event brings two equal and offsetting entries. Since these financial events are not automatically confirmed by both parties, the accounting quality can be improved. The blockchain mechanism possibly offers a different take on accounting. Based on an experimentation approach, data was collected to compare the double-entry method with the blockchain-based triple-entry method. The results show that the main difference concerns determining the completeness of the financial statement items. In the situation of double-entry accounting, segregation of duties is applied to do so. In the blockchain situation, the underlying mechanism of the blockchain already ensures this.
The accelerated growth of technological advancements has triggered the expansion of customer demand leading to highly complex supply chain networks. One viable way original equipment manufacturers (OEMs) can respond to changing purchasing habits is to redesign their strategic and operational activities to build far-reaching information and resource avenues allied with effective marketing policies. These newly implemented policies need to comply with extended producer responsibility (EPR) guidelines that also well align with rising consumer awareness towards green consumption. To achieve this, manufacturers must create efficient end-of-life product (EOLP) return structures and ensure value creation through product recovery operations to dwindle the cascading waste of discarded products. From an environmental viewpoint, retrieving the value embedded in returned items through remanufacturing or recycling has been proven to be effective in reducing the amount of industrial solid waste. EOLP processing operations are heavily reliant on customers' participation in returning outdated devices making product collection a crucial step in point-to-point supply chains. To entice end-users, the OEMs need to design environmentally and economically benign product take-back strategies that would spark the volume of product returns. These constraints dictate two structural challenges: how manufacturers and consumers can become active participants of EOLP treatment activities, and how fast and efficiently OEMs can respond to the changing market and capital needs while preserving their sustainability levels. In terms of active participation, trade-in incentives can help stimulate additional revenue channels for OEMs through product remanufacturing while helping companies comply with the EPR legislations. Trade-in policies are set forth as part of long-term marketing strategies and include incentive programs that aim at enticing current and potential customers to trade-in their used products with newer generations at a discounted price or for instant credit. Within the context of purchasing behavior, trade-in programs positively impact customers' buying decisions by granting buyers the ability to claim the scrap value of their existing devices. Particularly in oversaturated industries such as electronics and automotive, take-back incentives are a pipeline for OEMs to generate significant residual value by reselling remanufactured products on secondary markets. Moreover, offering special discounts or credits in lieu of old devices fuels new product sales by creating an additional revenue stream. Still, in today’s fast-changing market dynamics, inept trade-in practices that fail to eliminate the ambiguity surrounding the prediction of the true quality of returned products bring functional and financial burdens to organizations. The conventional intransigent trade-in schemes fail to address this uncertainty leading to a number of unnecessary inspection, disassembly, and shipment steps resulting in increasing complexity and product recovery cost. Achieving an accurate trade-in scheme is a highly complex multi-dimensional problem requiring novel solutions that traditional manufacturing and supply chain technologies are incapable of offering by design. Such challenging task inevitably necessitates strategic initiatives that stem from the utilization of cutting-edge groundbreaking information technologies for rapid response to customer needs and reduced complexity across all operational layers. Despite the numerous methodologies investigating the potential value gain from remanufacturing and product acquisition pricing policies, there is no study in related literature that incorporates trade-in programs into an intelligent remanufacturing structure. A majority of previous studies propose preventive models with pre-determined and rule-based explicit model parameters hindering the practicability of the substantial volume of data generated by the increased use of technological tools. These models, inevitably, fall short in successfully incorporating long-term manufacturing goals into sustainable business strategies. With this motivation, the architectural framework this dissertation introduces addresses a predictive product recovery model for product returns to enable an autonomous, sensor-embedded, and decentralized disassembly and remanufacturing system. The main objective of this research is to investigate the feasibility of cost- and resource-effective end-of-life product management systems in a smart reverse logistics network where trade-in rebate decisions take place in an autonomous ecosystem. This research, while filling the emerging gap in the utilization of current digital technologies to determine quality-dependent acquisition strategies, also provides a novel quantitative analysis on the efficiency of trade-in policymaking. This model can be employed in manufacturing industries for precise assessment of value creation amid digital advancements in a future-oriented platform. Due to its highly saturated formation, the consumer electronics industry offers a more suitable platform for this study. Therefore, this study examines a trade-in model for a specific technological product, game console, with the help of a case study. First phase of the dissertation evaluates the performance degradation pattern of discarded electronics products in a ubiquitous manner through timestamp data enablers. To handle this highly complex large-volume data, a discrete-event simulation model is developed from the original equipment manufacturer viewpoint. The model aims to examine the behavior of returned devices as well as the expected overall cost of product recovery operations. Following this, a design of experiments study is utilized for the experimentation using Taguchi’s Orthogonal Arrays (OAs). Employing the findings obtained in the first phase, the second phase of the study deals with trade-in policymaking to determine an engaging quotation for varying quality of returned products from the perspectives of all parties involved in the transaction. To achieve this, an initial model for trade-in-to-upgrade incentives is established for discrete sets of quality standards in case where returned products are grouped into three quality classes based on their usage time. The model is then expanded to compare two product acquisition strategies, namely, trade-in-to-upgrade incentives and instant credits. To achieve a realistic strategy, two rebate models are constructed in a simulation-based game setting to mimic the customer behavior and to obtain the resulting payoffs for the OEM in a dynamic ecosystem. To handle the uncertainty in the customer's decision towards the incentive offer, logistic regression analysis is conducted to maximize the likelihood of the acceptance rate. Finally, trade-in policies are compared to obtain favorable strategies augment revenue streams.
Open access
Manufacturing Process and Optimization
Product Development and Customization
Additive Manufacturing and 3D Printing Technologies
Smart contracts are appealing because they are self-executing business agreements between parties with the predefined and immutable obligations and rights. However, as with all software, smart contracts may contain vulnerabilities because of design flaws, which may be exploited by one of the parties to defraud the others. In this paper, we demonstrate a systematic approach to building secure design models for smart contracts using formal methods. To build the secure models, we first model the behaviors of participating parties as state machines, and then, we model the predefined obligations and rights of contracts, which specify the interactions among state machines for achieving the business goal. After that, we illustrate executable secure model design patterns in TLA+ (Temporal Logic of Actions) to against well-known smart contract vulnerabilities in terms of state machines and obligations and rights at the design level. These vulnerabilities are found in Ethereum contracts, including Call to the unknown, Gasless send, Reentrancy, Lost in the transfer, and Unpredictable state. The resultant TLA+ specifications are called secure models. We illustrate our approach to detect the vulnerabilities using a real-estate contract example at the design level.
Jaehong Ahn, Mingyu Park, Hyungsik Shin, Jeongyeup Paek
E-commerce has become a crucial part of our life allowing us to buy products, request services, and transfer money easily with a press of a button. As such, establishing immutable trust and reputation of entities that are resilient to manipulation by the malicious are critical in today’s online systems. In this work, we propose a model for calculating trust and reputation using the values stored on blockchain ledger. The model is applied to blockchain-based online payment systems which have a characteristic of immutability by preventing data manipulation. The model normalizes user evaluations based on each user’s personal evaluation criteria that changes over time. In addition, the model derives reputation of, and trust between, users by applying psychological factors. We evaluate our model using not only simulated transaction data but also on real Bitcoin transaction-based dataset to show that our model is able to derive stable values from immutable transactions on blockchain-based online payment systems. Our model has been built into a live commercial blockchain service platform, and new application developments are underway.
Smart contracts and blockchain technology have the potential to change tremendously the contractual practices. Together with the development of blockchain-based technology, many well-known companies and other private or public entities such as governments take advantage of smart contracts. On the one hand, there are costs connected with the programming and coding of smart contracts, or training those administering them, on the other hand, however, it seems to be true that smart contracts will bring greater certainty and extra cost saving for those who apply them in their business dealings. During recent years, rapid technological development has resulted in plenty of changes in the way how financial services are conducted. Blockchain technology is predicted to disrupt the current environment of business dealings by enabling the unprecedented ways to cooperate and communicate between the parties of the contract.
Canada has a stable financial system with large national banks that integrate fintech internally to improve operations, products and service. Because of the size and stability of its major banks, Canada has been criticized as lagging globally in consumer fintech adoption rates and fostering new fintech market entrants. The supervisory frameworks for non-bank fintech firms in Canada has some agency fragmentation costs, which can serve as a barrier to entry for new firms. The following report outlines the law of fintech, as it applies in Canada, including the regulatory frameworks for fintech money (e-money, virtual currency and electronic payments); fintech financings (initial coin offerings, peer to peer lending and invoice trading); and fintech financial services (smart contract investing, robo-advisors, algorithmic trading and market automation, artificial intelligence, decentralized autonomous organizations, and crypto hedge funds).
BACKGROUND: Health care professionals are required to maintain accurate health records of patients. Furthermore, these records should be shared across different health care organizations for professionals to have a complete review of medical history and avoid missing important information. Nowadays, health care providers use electronic health records (EHRs) as a key to the implementation of these goals and delivery of quality care. However, there are technical and legal hurdles that prevent the adoption of these systems, such as concerns about performance and privacy issues. OBJECTIVE: This study aimed to build and evaluate an experimental blockchain for EHRs, named HealthChain, which overcomes the disadvantages of traditional EHR systems. METHODS: HealthChain is built based on consortium blockchain technology. Specifically, three organizations, namely hospitals, insurance providers, and governmental agencies, form a consortium that operates under a governance model, which enforces the business logic agreed by all participants. Every peer node hosts an instance of the distributed ledger consisting of EHRs and an instance of chaincode regulating the permissions of participants. Designated orderers establish consensus on the order of EHRs and then disseminate blocks to peers. RESULTS: HealthChain achieves functional and nonfunctional requirements. It can store EHRs in a distributed ledger and share them among different participants. Moreover, it demonstrates superior features, such as privacy preservation, security, and high throughput. These are the main reasons why HealthChain is proposed. CONCLUSIONS: Consortium blockchain technology can help to build new EHR systems and solve the problems that prevent the adoption of traditional systems.
This paper aims to propose some behavioural biases of trading in Bitcoin. It is review literature in the areas of behavioural finance that address issues related to Bitcoin to underpin the conceptual model. A conceptual model for understanding the behavioural bias that affects investing in cryptocurrency is proposed. The biases are herding, optimism, overconfidence, confirmation bias, loss aversion, and gamblers’ fallacy. This paper ought to fill the research gap on cryptocurrency from the behavioral perspective. This paper implies that prices and Bitcoin transactions are more determined by psychological factors.
Constantinos Patsakis, Fran Casino, Nikolaos Lykousas, Vasilios Katos
The current landscape of the core Internet technologies shows considerable centralisation with the big tech companies controlling the vast majority of traffic and services. This has sparked a wide range of decentralisation initiatives with perhaps the most profound and successful being the blockchain technology. In the past years, a core Internet infrastructure, domain name system (DNS), is being revised mainly due to its inherent security and privacy issues. One of the proposed panaceas is Blockchain-based DNS, which claims to solve many issues of traditional DNS. However, this does not come without security concerns and issues, as any introduction and adoption of a new technology does - let alone a disruptive one such as blockchain. In this work, we discuss a number of associated threats, including emerging ones, and we validate many of them with real-world data. In this regard, we explore a part of the blockchain DNS ecosystem in terms of the browser extensions using such technologies, the chain itself (Namecoin and Emercoin), the domains, and users which have been registered in both platforms. Finally, we provide some countermeasures to address the identified threats, and we propose a fertile common ground for further research.
Michael Neuder, Daniel J. Moroz, Rithvik Rao, David C. Parkes
Proof-of-Stake consensus protocols give rise to complex modeling challenges. We analyze the Babylon update (October 2019) to the Proof-of-Stake protocol on the Tezos blockchain, and demonstrate that, under certain conditions, rational participants are incentivized to behave dishonestly. In doing so, we provide a theoretical analysis of the feasibility and profitability of a block stealing attack that we call selfish endorsing, a concrete instance of an attack previously only theoretically considered. We propose and analyze a simple change to the Tezos protocol which significantly reduces the (already small) profitability of this dishonest behavior, and introduce a new delay and reward scheme that is provably secure against length-1 and length-2 selfish endorsing attacks. Our framework provides a template for analyzing other Proof-of-Stake protocols for the possibility of selfish behavior.
The goal of this paper is to help mainstream programmers routinely use formal verification on their smart contracts by 1) proposing a new YAML-format for writing general-purpose formal specifications, 2) demonstrating how a formal specification can be incrementally built up without needing advanced training, and 3) showing how formal specifications can be tested by using program mutation.
Supply chain management or logistics is a crucial aspect of any business, be it agriculture, food, healthcare, and more. However, it also involves a communication among a host of actors performing different roles in the chain. Integration of these actors in a transparent yet secure way is needed, which blockchain technology has the potential to do so. In this article, different models of blockchain application in logistics are discussed and how the permissioned blockchains can be used in realizing the business case. An overview of different frameworks which enable permissioned blockchains are discussed in this article. Based on this, the authors propose an asset transfer model which can be employed to enhance supply chain trackability using permissioned blockchain.
The proliferation of unreliable and biased information is a significant\nproblem on the Internet. To assess the credibility of the information retrieved\nfrom news websites and other sources, users often resort to social platforms\nlooking for confirmation with trustworthy parties. However, users may be faced\nwith considerable obstacles posed by the platform provider, who can prevent\naccess to certain content. This paper presents DClaims, a system that provides\na censorship-resistant distributed service for the exchange of information over\nthe Internet using web annotations. DClaims' fully decentralized architecture\nrelies on Inter-Planetary File System (IPFS) and Ethereum blockchain, both of\nwhich offer desirable censorship resistant properties. DClaims is implemented\nas a web annotations browser extension which allows for the classification of\nnews articles, on news websites. From our evaluation of the system, we conclude\nthat a large scale implementation of the system is practical and economically\nviable.\n
Consensus mechanism is the heart of any blockchain network. Many projects have proposed alternative protocols to improve restricted scalability of Proof of Work originated since Bitcoin. As an improvement of Delegated Proof of Stake, in this paper, we introduce a novel consensus, namely, Delegated Proof of Reputation, which is scalable, secure with an acceptable decentralization. Our innovative idea is replacing pure coinstaking by a reputation ranking system essentially based on ranking theories (PageRank, NCDawareRank and HodgeRank).
This paper studies simple moving average trading strategies employing daily price data on the eleven most-traded cryptocurrencies in the 2016–2018 period. Our results indicate a variable moving average strategy is successful when using the 20 days moving average trading strategy. Specifically, excluding Bitcoin the technical trading rule generates an excess return of 8.76% p.a. after controlling for the average market return. Our results suggest that cryptocurrency markets are inefficient.
Participatory sensing is gaining popularity as a method for collecting and sharing information from distributed local environments using sensor-rich mobile devices. There are a number of participatory sensing applications currently in wide use, such as location-based service applications (e.g., Waze navigation). Usually, these participatory applications collect tremendous amounts of sensing data containing personal information, including user identity and current location. Due to the high sensitivity of this information, participatory sensing applications need a privacy-preserving mechanism, such as anonymity, to secure and protect personal user data. However, using anonymous identifiers for sensing sources proves difficult when evaluating sensing data trustworthiness. From this perspective, a successful participatory sensing application must be designed to consider two challenges: (1) user privacy and (2) data trustworthiness. To date, a number of privacy-preserving reputation techniques have been proposed to satisfy both of these issues, but the protocols contain several critical drawbacks or are impractical in terms of implementation. In particular, there is no work that can transparently manage user reputation values while also tracing anonymous identities. In this work, we present a blockchain-based privacy-preserving reputation framework called BPRF to transparently manage user reputation values and provide a transparent tracing process for anonymous identities. The performance evaluation and security analysis show that our solution is both practical and able to satisfy the two requirements for user privacy and data trustworthiness.