Antonio LĂłpez Vivar, Alberto TurĂŠgano Castedo, Ana Lucila Sandoval Orozco, Luis Javier GarcĂa Villalba
Smart contracts have gained a lot of popularity in recent times as they are a very powerful tool for the development of decentralised and automatic applications in many fields without the need for intermediaries or trusted third parties. However, due to the decentralised nature of the blockchain on which they are based, a series of challenges have emerged related to vulnerabilities in their programming that, given their particularities, could have (and have already had) a very high economic impact. This article provides a holistic view of security challenges associated with smart contracts, as well as the state of the art of available public domain tools.
Rameshwar Dubey, Angappa Gunasekaran, David Bryde, Yogesh K. Dwivedi ¡ 5 authors
There has been tremendous interest in blockchain technology (BT) (also known as distributed ledger technology) around the globe and across sectors. Following significant success in the financial sector, other sectors, such as humanitarian sector, have started deploying BT at various levels. Although the use of BT in the humanitarian sector is in its infancy, donors and government agencies are increasingly calling for building BT-enabled swift-trust (ST) and more collaborative relationships among various humanitarian actors in order to improve the transparency and traceability of disaster relief materials, information exchanges and flow of funds in disaster relief supply chains. Our study, which is informed by organisational information processing theory and relational view, proposes a theoretical model to understand how BT can influence operational supply chain transparency (OSTC) and ST among actors engaged in disaster relief operations. Our model also shows how BT-enabled ST can further improve collaboration (CO) among actors engaged in disaster relief operations and enhance supply chain resilience (SCR). We formulated and tested six research hypotheses, using data gathered from international non-governmental organisations with the help of the Coordinator for Humanitarian Affairs (OCHA) database. We received 256 usable responses using a pre-tested survey-based instrument designed for key informants. Our results confirm that our six hypotheses were supported. Our study offers significant and valid contributions to the literature on ST, CO and SCR and BT/distributed ledger technology. We have also noted the limitations of our study and have offered future research directions.
This paper presents IBFT, a simple and elegant Byzantine fault-tolerant consensus algorithm that is used to implement state machine replication in the \emph{Quorum} blockchain. IBFT assumes a partially synchronous communication model, where safety does not depend on any timing assumptions and only liveness depends on periods of synchrony. The algorithm is deterministic, leader-based, and optimally resilient - tolerating $f$ faulty processes out of $n$, where $n \geq 3f+1$. During periods of good communication, IBFT achieves termination in three message delays and has $O(n^2)$ total communication complexity.
Nazar Waheed, Xiangjian He, Muhammad Ikram, Muhammad Usman ¡ 6 authors
Security and privacy of the users have become significant concerns due to the involvement of the Internet of things (IoT) devices in numerous applications. Cyber threats are growing at an explosive pace making the existing security and privacy measures inadequate. Hence, everyone on the Internet is a product for hackers. Consequently, Machine Learning (ML) algorithms are used to produce accurate outputs from large complex databases, where the generated outputs can be used to predict and detect vulnerabilities in IoT-based systems. Furthermore, Blockchain (BC) techniques are becoming popular in modern IoT applications to solve security and privacy issues. Several studies have been conducted on either ML algorithms or BC techniques. However, these studies target either security or privacy issues using ML algorithms or BC techniques, thus posing a need for a combined survey on efforts made in recent years addressing both security and privacy issues using ML algorithms and BC techniques. In this paper, we provide a summary of research efforts made in the past few years, starting from 2008 to 2019, addressing security and privacy issues using ML algorithms and BCtechniques in the IoT domain. First, we discuss and categorize various security and privacy threats reported in the past twelve years in the IoT domain. Then, we classify the literature on security and privacy efforts based on ML algorithms and BC techniques in the IoT domain. Finally, we identify and illuminate several challenges and future research directions in using ML algorithms and BC techniques to address security and privacy issues in the IoT domain.
Tasos Bakogiannis, Ioannis Mytilinis, Katerina Doka, Georgios Goumas
Cloud computing offerings traditionally originate from a handful of large and well established providers, which monopolize the market, preventing small players and individuals from having a share. As a result, the few, blindly and perforce trusted entities define the prices and manage to gain a significant competitive advantage by exploiting the knowledge derived by usersâ data and computations. To tackle this monopoly and empower the democratization and full decentralization of the cloud computing market, we present CloudAgora, a platform that enables any potential resource provider, ranging from individuals to large companies, to monetize idle resources competing on equal terms, and allows any cloud consumer to enjoy access to low-cost storage and computation without having to trust any central authority. The key enabler of the platform is Blockchain technology, which is used to record commitment policies through the use of smart contracts, publicly verify off-chain services, both storage and computation related, and trigger automatic micropayments. On one hand, cloud consumers have the chance to request storage or compute resources, upload data, and outsource task processing over remote, fully distributed infrastructures. Although such infrastructures cannot be a priori trusted, CloudAgora offers mechanisms to ensure the verifiable validity of the outsourced storage and computation, discourage potential providers from behaving maliciously, and incentivize participants to play fair. On the other hand, providers are able to participate in auctions, placing bids for storage or computation tasks, serve requests, and offer validity proofs upon request. Our prototype is built as a Dapp on top of Ethereum and is available as an open source project.
Green public procurement (GPP) is a policy tool aiming to achieve environmental protection and resource reservation via public procurement. After decades of adaptation, what promotes and hinders its uptake in public contracting remains difficult to discern. This research explores factors that influence the adoption of green award criteria, covering features of procurement procedures, purchasers, tenderers, and the business sectors through empirical analysis of Probit regression combined with a fixed term method. The data is contract award notices (CAN) from 33 countries in Europe in 2018. Our findings suggest that framework agreements, the medical products sector, the health and social services sector, and the business services sector are negatively correlated with whether a contract is green. On the other hand, the contract value, Government Procurement Agreement (GPA)coverage, joint procurement, competitive dialogue, negotiation with competition (with a call for competition), restricted procedure, transport equipment sector, and food sector can positively correlate with green contracts, or these factors increase the possibility of a contract being green. Explicit explanations on these relations are provided. This research identifies factors relating with and influencing the application of green award criteria in public contracts, which would inform public sectors on efficient resources allocation in terms of increasing green public procurement performance.
Mahdi Daghmehchi Firoozjaei, Ali A. Ghorbani, Hyoungshick Kim, JaeSeung Song
In the current centralized IoT ecosystems, all financial transactions are routed through IoT platform providers. The security and privacy issues are inevitable with an untrusted or compromised IoT platform provider. To address these issues, we propose Hy-Bridge, a hybrid blockchain-based billing and charging framework. In Hy-Bridge, the IoT platform provider plays no proxy role, and IoT users can securely and efficiently share a credit with other users. The trustful end-to-end functionality of blockchain helps us to provide accountability and reliability features in IoT transactions. Furthermore, with the blockchain-distributed consensus, we provide a credit-sharing feature for IoT users in the energy and utility market. To provide this feature, we introduce a local block framework for service management in the credit-sharing group. To preserve the IoT usersâ privacy and avoid any information leakage to the main blockchain, an interconnection position, called bridge, is introduced to isolate IoT usersâ peer-to-peer transactions and link the main blockchain to its subnetwork blockchain(s) in a hybrid model. To this end, a k-anonymity protection is performed on the bridge. To evaluate the performance of the introduced hybrid blockchain-based billing and charging, we simulated the energy use case scenario using Hy-Bridge. Our simulation results show that Hy-Bridge could protect user privacy with an acceptable level of information loss and CPU and memory usage.
Federico Caviggioli, Lucio Lamberti, Paolo Landoni, Paolo Meola
Purpose Evidence from previous literature indicates that adopting a new innovative technology has a positive impact on a companyâs business performance. Much less work has been carried out into examining whether a technology adoption has impact on corporate reputation. This paper aims to examine the latter topic in a context where social media is the channel used to share news about the introduction of a new technology. The empirical setting of the study consists of five retail companies located in the USA that decided to include Bitcoin as a payment platform. Design/methodology/approach Twitter data were used to measure how sharing news about the adoption of new technology could affect the reputation of the companies selected, keeping a clear distinction between the volume of data relating to social media responses and the sentiment expressed in the tweets. A panel vector autoregression model was used to incorporate series of data relating to news items, volume and sentiment. Findings The results show that the news about the adoption of a new technology has a positive impact on both the volume of tech-related tweets and the sentiment expressed in the tweets themselves, although the patterns of these two effects are different. The resulting impact decreases after a few days, both in volume and in sentiment. Research limitations/implications The analysis has limitations that future research could address by extending and diversifying the examined companies and the social media used as data sources. The research suggests that managers in medium-sized companies can leverage on the introduction of new technologies that have a direct impact on their customers and gain reputational benefits in terms of immediate visibility. Originality/value The research introduces an additional dimension of analysis to the current stream of corporate reputation. Although the literature has already covered the dynamics of response to events on Twitter, by focusing on the adoption of the new Bitcoin technology, the paper provides novel insights.
Open access
Digital Marketing and Social Media
Corporate Identity and Reputation
Consumer Behavior in Brand Consumption and Identification
This paper investigates the factors influencing the intention to adopt cryptocurrency payments among small to medium-sized enterprises (SMEs) in tourism and hospitality through the lens of the technology acceptance model (TAM). This paper uses an original sample of 101 SMEs employing a total of 15,831 people in Taiwan. Structural equation modeling is used to examine the joint effect of both internal and external factors influencing the intention to use cryptocurrency payments. Results show that (1) strategic orientation, owner/managers personal characteristics (self-efficacy and innovativeness) and social influence have a strong effect on the intention to adopt new technology; (2) perceived usefulness mediates the effects of strategic orientation and social influence; and (3) perceived ease of use mediates the effect of self-efficacy on the intention to adopt cryptocurrency payments. The present study is one of few empirical inquiries about cryptocurrency payment adoption among SMEs. This study extends the theoretical foundations of the TAM into the specific characteristics of SMEs. Limitations of the study are sample size and a single survey design. However, findings of this research on the cryptocurrency payment adoption offer practical implications for tourism stakeholders towards supporting SMEs competitiveness. The originality of this study is based on the fact that cryptocurrency payment is a new technology, and on the potential of cryptocurrency payments to disrupt the traditional way of operating tourism and hospitality SMEs. Hence the importance to consider major factors influencing SMEsâ intentions to adopt this technology.
Abstract Bitcoin is currently the leading global provider of cryptocurrency. Cryptocurrency allows users to safely and anonymously use the Internet to perform digital currency transfers and storage. In recent years, the Bitcoin network has attracted investors, businesses, and corporations while facilitating services and product deals. Moreover, Bitcoin has made itself the dominant source of decentralized cryptocurrency. While considerable research has been done concerning Bitcoin network analysis, limited research has been conducted on predicting the Bitcoin price. The purpose of this study is to predict the price of Bitcoin and changes therein using the grey system theory. The first order grey model (GM (1,1)) is used for this purpose. It uses a first-order differential equation to model the trend of time series. The results show that the GM (1,1) model predicts Bitcoinâs price accurately and that one can earn a maximum profit confidence level of approximately 98% by choosing the appropriate time frame and by managing investment assets.
We study the problem of state machine replication (SMR)---the underlying problem addressed by blockchain protocols---in the presence of a malicious adversary who can corrupt some fraction of the parties running the protocol. Existing protocols for this task assume either a synchronous network (where all messages are delivered within some known time $Î$) or an asynchronous network (where messages can be delayed arbitrarily). Although protocols for the latter case give seemingly stronger guarantees, this is not the case since they (inherently) tolerate a lower fraction of corrupted parties. We design an SMR protocol that is network-agnostic in the following sense: if it is run in a synchronous network, it tolerates $t_s$ corrupted parties; if the network happens to be asynchronous it is resilient to $t_a \leq t_s$ faults. Our protocol achieves optimal tradeoffs between $t_s$ and $t_a$.
Yunan Ye, Hengzhi Pei, Boxin Wang, PinâYu Chen ¡ 7 authors
Portfolio management (PM) is a fundamental financial planning task that aims to achieve investment goals such as maximal profits or minimal risks. Its decision process involves continuous derivation of valuable information from various data sources and sequential decision optimization, which is a prospective research direction for reinforcement learning (RL). In this paper, we propose SARL, a novel State-Augmented RL framework for PM. Our framework aims to address two unique challenges in financial PM: (1) data heterogeneity -- the collected information for each asset is usually diverse, noisy and imbalanced (e.g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary. To incorporate heterogeneous data and enhance robustness against environment uncertainty, our SARL augments the asset information with their price movement prediction as additional states, where the prediction can be solely based on financial data (e.g., asset prices) or derived from alternative sources such as news. Experiments on two real-world datasets, (i) Bitcoin market and (ii) HighTech stock market with 7-year Reuters news articles, validate the effectiveness of SARL over existing PM approaches, both in terms of accumulated profits and risk-adjusted profits. Moreover, extensive simulations are conducted to demonstrate the importance of our proposed state augmentation, providing new insights and boosting performance significantly over standard RL-based PM method and other baselines.
With the rapid development of the Internet of Things (IoT), the number of IoT devices has increased exponentially. Therefore, we have put forward higher security requirements for the management, transmission, and storage of massive IoT data. However, during the transmission process of IoT data, security issues, such as data theft and forgery, are prone to occur. In addition, most existing data storage solutions are centralized, i.e., data are stored and maintained by a centralized server. Once the server is maliciously attacked, the security of IoT data will be greatly threatened. In view of the above-mentioned security issues, a security transmission and storage solution is proposed about sensing image for blockchain in the IoT. Firstly, this solution intelligently senses user image information, and divides these sensed data into intelligent blocks. Secondly, different blocks of data are encrypted and transmitted securely through intelligent encryption algorithms. Finally, signature verification and storage are performed through an intelligent verification algorithm. Compared with the traditional IoT data transmission and centralized storage solution, our solution combines the IoT with the blockchain, making use of the advantages of blockchain decentralization, high reliability, and low cost to transfer and store users image information securely. Security analysis proves that the solution can resist theft attacks and ensure the security of user image information during transmission and storage.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
The cryptocurrency market is amongst the fastest-growing of all the financial markets in the world. Unlike traditional markets, such as equities, foreign exchange and commodities, cryptocurrency market is considered to have larger volatility and illiquidity. This paper is inspired by the recent success of using deep learning for stock market prediction. In this work, we analyze and present the characteristics of the cryptocurrency market in a high-frequency setting. In particular, we applied a deep learning approach to predict the direction of the mid-price changes on the upcoming tick. We monitored live tick-level data from $8$ cryptocurrency pairs and applied both statistical and machine learning techniques to provide a live prediction. We reveal that promising results are possible for cryptocurrencies, and in particular, we achieve a consistent $78\%$ accuracy on the prediction of the mid-price movement on live exchange rate of Bitcoins vs US dollars.
Since Bitcoin's inception in 2008, it has became attractive investments for both trading and mining. To mine Bitcoins, a miner has to invest in computing power and pay for electricity to solve cryptographic puzzles for rewards, if it becomes the first to solve a puzzle, paid in Bitcoin. Given that mining is such a resource intensive effort, miners seek new strategies trying to make the mining process more profitable.
Proof-of-vote is a third generation of the Nakamoto consensus. With proof-of-vote, validators compete for people-votes, using proof-of-suffrage given by proof-of-person, and authorize transactions based on authority delegated by the consensus mechanism, just like proof-of-work or proof-of-stake. This logical conclusion of the Nakamoto consensus allows a ânationâ of people to secure their own ledger, the equivalent of representative democracy for distributed ledger technology.
Vladimir Soloviev, Symon P. Yevtushenko, Viktor Batareyev
This article demonstrates the comparative possibility of constructing indicators of critical and crash phenomena in the volatile market of cryptocurrency and developed stock market. Then, combining the empirical cross-correlation matrix with the Random Matrix Theory, we mainly examine the statistical properties of cross-correlation coefficients, the evolution of the distribution of eigenvalues and corresponding eigenvectors in both markets using the daily returns of price time series. The result has indicated that the largest eigenvalue reflects a collective effect of the whole market, and is very sensitive to the crash phenomena. It has been shown that introduced the largest eigenvalue of the matrix of correlations can act like indicators-predictors of falls in both markets.
Pu Yuan, Kan Zheng, Xiong Xiong, Kuan Zhang ¡ 5 authors
As a highly scalable permissioned blockchain platform, Hyperledger Fabric supports a wide range of industry use cases ranging from governance to finance. In this paper, we propose a model to analyze the performance of a Hyperledgerbased system by using Generalised Stochastic Petri Nets (GSPN). This model decomposes a transaction flow into multiple phases and provides a simulation-based approach to obtain the system latency and throughput with a specific arrival rate. Based on this model, we analyze the impact of different configurations of ordering service on system performance to find out the bottleneck. Moreover, a mathematical configuration selection approach is proposed to determine the best configuration which can maximize the system throughput. Finally, extensive experiments are performed on a running system to validate the proposed model and approaches.
The traditional method of blockchain retrieval is to search the âBlock Fileâ in sequence from the âtailâ to the âheadâ of the blockchain, which always takes a lot of time. How to reduce the retrieval time has been a hot issue in blockchain research. This paper proposes a fast retrieval method for the Multi-Transaction Mode Consortium Blockchain (MTMCB). Firstly, we create a âUser Setâ and âBlock Name Setâ cached in Redis. Then, according to the transaction participants and âBlock Name Setâ, we can get the relevant âBlock Name Listâ, and quickly obtain the corresponding block files. On this basis, in order to meet the needs of rapid retrieval in large-scale systems, an improved retrieval algorithm based on a B+-tree data structure is proposed. Firstly, the block file information is put into different ordered sets according to the transaction participants, and the B+-tree index is established to quickly get the information of relevant block files by participants. Experimental results show that the improved method of Redis cache retrieval in this paper can greatly increase the efficiency of blockchain retrieval, and can settle some crucial problem in the blockchain application and popularization.
Blockchain is a decentralized transaction and data management technology. It was developed for the world's first cryptocurrency known as Bitcoin in 2008. The reason behind its popularity was its properties which provide pseudonymity, security, and data integrity without third-party intervention. Initially, most of the researches were focused on the Bitcoin system and its limitation, but later other applications of Blockchain e.g. smart contracts and licensing [1] also got famous. Blockchain technology has the potential to change the way how transactions are conducted in daily life. It is not limited to cryptocurrencies but could be possibly applied in various environments where any forms of transactions are done. This article presents a comprehensive overview of Blockchain technology, its development, applications, security issues, and their countermeasures. In particular, the security towards illegal data insertion and the countermeasures is focused. Our analysis of countermeasures of illegal data insertion can be combined for increased efficiency. After the introduction of the Blockchain and consensus algorithm, some famous Blockchain applications and expected future of Blockchain are deliberated. Then, the technical challenges of Blockchain are discussed, in which the main focus here is on the security and the data insertion in Blockchain. The review of the possible countermeasures to overcome the security issues related to data insertion are elaborated.
Saidjahon Hayrutdinov, Mahmoud S. R. Saeed, Azamat Rajapov
The study proposes a supply chain contractual coordination model based on the product lifecycle information sharing effort and consumers' price sensitivity to a product with the Blockchain system. This paper examined the following five scenarios: (1) centralized supply chain with Blockchain system-based product lifecycle information sharing investment; (2) Stackelberg leader retailer processed and invested Blockchain system scenario; (3) retailer processed the Blockchain system cost-sharing scenario; (4) retailer processed Blockchain system investment through bargaining the revenue-sharing model; (5) Blockchain system investment under the cost and revenue-sharing contract. The study used the game theory reverse induction method to compare the Nash equilibrium solutions under different decision-making scenarios and discussed the chain memberâs constraint condition of Blockchain system investment. We simulated and analysed the productsâ lifecycle information sharing effort cost factor, the influence of price sensitivity coefficient, and expected profits of the supplier and retailer. The study results show that the product lifecycle information sharing effort under the Blockchain system increases the profit of the whole chain and decreases with the increase of customerâs price sensitivity coefficient.
The purpose of this Note is to determine which cryptocurrency initial distribution methods involve the offering of securities as regulated by the 1933 Securities Act. The primary legal issue is the Howey test. This test identifies whether an offering is an investment contract, and thus subject to regulation by the 1933 Securities Act, based on whether it involves an investment of money in a common enterprise, in which investors are led to expect profits from the efforts of a promoter or third party. The distribution methods discussed are mining, airdropping, forking, and initial coin offerings (âICOsâ). Mining, airdropping and forking are likely not investment contracts, but initial coin offerings likely are. However, regulators should make it clear that mining, airdropping and forking are acceptable practices. Furthermore, they should proceed with a light touch when regulating initial coin offerings, except in the case of fraud. In particular, the ICO community in partnership with government should instigate a system where âcrypto-underwritersâ vet ICOs and the crypto-underwriters are regulated by the SEC.
Andrey Pogudin, Anindya S. Chakrabati, Tiziana Di Matteo
Cryptocurrencies represent an asset class featuring two unique properties: they are not backed by sovereigns, and their supply is fixed exogenously. This combination becomes apparent in their volatility, which is driven only by demand-side factors. In particular, cryptocurrencies represent an extreme case of the excess volatility puzzle, with asset prices moving more than the fundamentals. We explore the effects of market capitalization on the dynamics of cryptocurrencies within both returns and volatility networks and show that these cryptocurrencies exhibit scaling properties in volatility with respect to market capitalization. The dependency network suggests that currencies with a larger market share have a larger presence in the dominant eigenspectrum, and they exert more influence in the comovement network. In these regards, we find parallels between the dynamics of cryptocurrencies and those of more traditional asset classes. Our findings have implications for both researchers and practitioners in terms of modeling and analyzing the collective behavior of financial assets.
Automotive software is increasingly complex and critical to safe vehicle operation, and related embedded systems must remain up-to-date to ensure long-term system performance. Update mechanisms and data modification tools introduce opportunities for malicious actors to compromise these cyber-physical systems, and for trusted actors to mistakenly install incompatible software versions. A distributed and stratified "black box" audit trail for automotive software and data provenance is proposed to assure users, service providers, and original equipment manufacturers (OEMs) of vehicular software integrity and reliability. The proposed black box architecture is both layered and diffuse, employing distributed hash tables (DHT), a parity system and a public blockchain to provide high resilience, assurance, scalability, and efficiency for automotive and other high-assurance systems.