Cloud is a computing model that provides sharing and supports ubiquitous on-demand access computing, providing new data processing and services for many industries, significantly reducing user computing and storage costs, and improving ease of use. With the development of cloud-scale and intensification, cloud security has become an essential issue in the field of cloud computing. Access control is one of the critical security technologies for protecting sensitive data stored in the cloud by enterprises and individuals. Since the centralized access control mechanism is adopted in the cloud, the sensitive data in the cloud are easy to be tampered with or leaked by hackers or cloud internal managers. To address this issue, we propose a blockchain-based access control framework with privacy protection called AuthPrivacyChain. Firstly, we use the account address of the node in blockchain as the identity, and at the same time, redefine the access control permission of data for the cloud, which is encrypted and stored in blockchain. After that, we design processes of access control, authorization, and authorization revocation in AuthPrivacyChain. Finally, we implement AuthPrivacyChain based on enterprise operation system (EOS), and the results show that AuthPrivacyChain can not only prevent hackers and administrators from illegally accessing resources, but also protect authorized privacy.
Garud Iyengar, Fahad Saleh, Jay Sethuraman, Wenjun Wang
We construct an economic framework for understanding the incentives of the participants of a permissioned blockchain for supply chains and other related industries. Our study aims to determine whether adoption of blockchain is socially beneficial and whether such adoption arises in equilibrium. We find that blockchain reduces information asymmetry for consumers, thereby enhancing consumer welfare. Consumer welfare gains can be sufficiently large that blockchain adoption is socially beneficial; nonetheless, we find that blockchain adoption does not arise in equilibrium. This situation arises because blockchain adoption costs are borne by manufacturers, and manufacturers cannot extract consumer gains through prices due to the competitive nature of the manufacturing sector. We offer a system of transfers to generate blockchain adoption in equilibrium when it is socially beneficial. This paper was accepted by Vishal Gaur, operations management. Funding: This research was partially supported by a seed grant from the Columbia–IBM Center for Blockchain and Data Transparency. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4532 .
Ariah Klages‐Mundt, Dominik Harz, Lewis Gudgeon, Junyou Liu · 5 authors
Stablecoins are one of the most widely capitalized type of cryptocurrency. However, their risks vary significantly according to their design and are often poorly understood. We seek to provide a sound foundation for stablecoin theory, with a risk-based functional characterization of the economic structure of stablecoins. First, we match existing economic models to the disparate set of custodial systems. Next, we characterize the unique risks that emerge in non-custodial stablecoins and develop a model framework that unifies existing models from economics and computer science. We further discuss how this modeling framework is applicable to a wide array of cryptoeconomic systems, including cross-chain protocols, collateralized lending, and decentralized exchanges. These unique risks yield unanswered research questions that will form the crux of research in decentralized finance going forward.
As smart contract platforms autonomously manage billions of dollars of capital, quantifying the portfolio risk that investors engender in these systems is increasingly important. Recent work illustrates that Proof of Stake (PoS) is vulnerable to financial attacks arising from on-chain lending and has worse capital efficiency than Proof of Work (PoW) \cite{fanti_pos_econ}. Numerous methods for improving capital efficiency have been proposed that allow stakers to create fungible derivative claims on their staked assets. In this paper, we construct a unifying model for studying the security risks of these proposals. This model combines birth-death Pólya processes and risk models adapted from the credit derivatives literature to assess token inequality and return profiles. We find that there is a sharp transition between 'safe' and 'unsafe' derivative usage. Surprisingly, we find that contrary to \cite{fanti2019compounding} there exist conditions where derivatives can \emph{reduce} concentration of wealth in these networks. This model also applies to Decentralized Finance (DeFi) protocols where staked assets are used as insurance. Our theoretical results are validated using agent-based simulation.
Automated market makers, first popularized by Hanson's logarithmic market scoring rule (or LMSR) for prediction markets, have become important building blocks, called 'primitives,' for decentralized finance. A particularly useful primitive is the ability to measure the price of an asset, a problem often known as the pricing oracle problem. In this paper, we focus on the analysis of a very large class of automated market makers, called constant function market makers (or CFMMs) which includes existing popular market makers such as Uniswap, Balancer, and Curve, whose yearly transaction volume totals to billions of dollars. We give sufficient conditions such that, under fairly general assumptions, agents who interact with these constant function market makers are incentivized to correctly report the price of an asset and that they can do so in a computationally efficient way. We also derive several other useful properties that were previously not known. These include lower bounds on the total value of assets held by CFMMs and lower bounds guaranteeing that no agent can, by any set of trades, drain the reserves of assets held by a given CFMM.
Advancements in the digital domain, for example in blockchain technology, big data and machine learning, are increasingly shaping the lives of individuals, groups, organizations, and societies. These developments call for effective governance to protect the basic interests and needs of these actors. Simultaneously, the very nature of governance is also changing. Policy-making is increasingly moving away from top-down governance by the state towards more horizontal modes of governance. This paper reviews the literature on governance theory in order to conceptualize governance as a mode of decentralized, networked regulation. We argue that the current dominant modes of governance are inadequate in understanding governance in the digital domain, and are poorly equipped to conceptualize novel forms of governance such as decentralized autonomous organizations (DAOs). Therefore, this study proposes a new mode of governance based on the regulation of new power relationships between the state and actors in the digital domain. This model further explores the role that blockchain technology can play in what we term decentralized network governance.
Most carbon Emission Trading Systems (ETS) rely on a centralized system to manage the transactional tasks, and are vulnerable to security threats. This article proposes a Blockchain-enabled Distributed ETS (BD-ETS) to improve the security and efficiency of the system. The BD-ETS transforms the centralized Carbon Emissions Permit (CEP) trading mode to a distributed trading system in which the trading mode is based on a smart contract performed in Hyperledger Fabric. In a smart contract, every transaction considers both the offer price and reputation value of the emitting enterprises. The voting power of the emitting enterprise is determined by its reputation value, which stems from their contributions to carbon emission reduction. To achieve consistency of every node in the CEP transactions, we propose a Delegated Proof of Reputation (DPoR) consensus mechanism. Compared to the enhanced Delegated Proof of Stake, the DPoR decreases the attack intention of malicious enterprises and performs better in finding malicious miners faster, thus improving the security of the BD-ETS. A case study and numerical simulations are developed to illustrate how the CEP trading functions, and to validate the DPoR mechanism.
This paper introduces the PoSH Consensus protocol, a novel work-in-progress construction for achieving Sybil-resistant Nakamoto-style probabilistic consensus on the contents of a cryptocurrency ledger in a permissionless decentralized network where parties stake their hardware’s computational power towards participation in leader election. PoSH aims to establish an openly mintable cryptocurrency that eliminates the requirement for block rewards and disincentivizes mining pools.
L. Byvsheva, O. Kondratenko, Yu. Samsonenko, M. Harasaylo
The article considers the strategic directions of financing the social sphere. It is determined that the effectiveness of the provision and financing of social services depends on the effectiveness of regional and local authorities and their ability to fulfill certain powers and responsibilities in a decentralized environment. At the same time, the processes of social policy reform are carried out in parallel with the process of strategic choice of the most effective model of development of the social protection system, which leads to losses and reduced efficiency of the system. The research of directions of budgetary financing of social sphere at the state level is carried out. The financial mechanism of realization of social protection which provides formation of the sources of the financial resources directed on performance of the corresponding programs and actions, and also the substantiated choice of methods, forms and levers of their use is investigated. The development of social services in the united territorial communities of Ukraine as of October 10, 2019 is analyzed. Expenditures on social protection and social security in the expenditures of the consolidated budget and GDP in Ukraine in the analyzed period are studied. Analysis of the state of filling local budgets in the context of decentralization in recent years has shown an increase in local budget revenues, which has created additional conditions for increasing funding for the social sphere from local budgets. It is determined that the main strategic directions of improving the mechanism of financial support of the social sphere in the conditions of decentralization should be: strengthening the responsibility for misuse of funds; optimal delimitation of spending powers between authorities at all levels; involvement of non-governmental organizations in the provision of social services in order to improve their quality, etc.
Richard Bluhm, Andreas Fuchs, Austin Strange, Axel Dreher · 6 authors
This paper studies the causal effect of transport infrastructure on the spatial distribution of economic activity within subnational regions across a large number of developing countries. To do so, we introduce a new global dataset of geolocated Chinese grant- and loan-financed development projects from 2000 to 2014 and combine it with measures of spatial concentration based on remotely sensed data. We find that Chinese-financed transportation projects decentralize economic activity within regions, as measured by a spatial Gini coefficient , by 2.2 percentage points. The treatment effects are particularly strong in regions that are less developed, more urbanized, and located closer to cities.
Jan 1, 2020·Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences
Sarra Alqahtani, Xinchi He, Rose Gamble, Papa Mauricio
The smart contract technology has increasingly attracted the attention of different industries. However, a significant number of smart contracts deployed in practice suffer from several bugs, which enable malicious users to cause damage. The research community has shifted their focus to verifying the correctness of smart contracts using model checkers and formal verification methods. The majority of the research investigates the correctness of systems built on one smart contract. This paper proposes a verification approach for systems composed of interacting smart contracts developed and controlled by different entities. We use the NuSMV model checker and the Behavioral Interaction Priority tool to model the behaviors of smart contracts and their interactions with the aim of verifying their compliance with the systems’ functional requirements. These requirements are formalized by Linear Temporal Logic propositions. The applicability of our approach is illustrated using a case study from The American Petroleum Institute and implemented using Hyperledger Fabric.
Smart contracts can be regarded as one of the most popular blockchain-based applications. The decentralized nature of the blockchain introduces vulnerabilities absent in other programs. Furthermore, it is very difficult, if not impossible, to patch a smart contract after it has been deployed. Therefore, smart contracts must be formally verified before they are deployed on the blockchain to avoid attacks exploiting these vulnerabilities. There is a recent surge of interest in analyzing and verifying smart contracts. While most of the existing works either focus on EVM bytecode or translate Solidity contracts into programs in intermediate languages for analysis and verification, we believe that a direct executable formal semantics of the high-level programming language of smart contracts is necessary to guarantee the validity of the verification. In this work, we propose a generalized formal semantic framework based on a general semantic model of smart contracts. Furthermore, this framework can directly handle smart contracts written in different high-level programming languages through semantic extensions and facilitates the formal verification of security properties with the generated semantics.
The progress of ICT technologies, day-ahead forecast, home energy management systems, implementation of smart meters, and Distributed Energy Sources (DER) enables new business opportunities for prosumers to locally trade the surplus via blockchain platforms leading to considerable advantages at the community level. The current research handles settlement similar to a centralized market that it is not necessarily the best solution for blockchain. Nonetheless, the settlement is essential as sellers and buyers perceive the attractiveness of the local trading through the market results. In this paper, we propose two novel and efficient settlement mechanisms (Global Balancing Settlement GBS and Splitting Settlement SS) for Peer-to-Peer (P2P) electricity exchange enhancing the performance of the classic Pairwise Settlement PS. These will be written as stored procedures embedded into the smart contracts along with auctioning procedures. The simulations are performed using a small residential community with 30% of the electricity that can be locally traded to lower the bills and unstress the public grid. The performance of the two proposed settlement methods is proved by the 14 scenarios that thoroughly indicate that GBS and SS provide better results for both sellers and buyers than PS. In the reference scenario, with GBS, sellers have the highest encashments with almost 4% more, whereas buyers encounter the lowest payments with almost 5% less than in case of the classic settlement. Starting from reference scenario, alternative scenarios are envisioned to extend the analyses and assess the performance of the settlement mechanisms. The highest gain is recorded with GBS mechanism: almost 8.8% for sellers and 6.5% for buyers. Another interesting outcome is that GBS is providing better results than SS. When deviations are small, SS provides almost 6% gain for both sellers and buyers, but when they increase, the gain is exceedingly small or none.
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.
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.
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.
Tetiana Tarasova, Olga Usatenko, Андрій Макурін, Valentyna Ivanenko · 5 authors
The objective of this paper is to determine the basic features of the use of mathematical modeling of the system to forecast cryptocurrency exchange rate. The study determines that cryptocurrency is the simplest investment asset being and, at the same time, the riskiest one. Advantages and disadvantages of cryptocurrency use are specified. Advantages include: code openness, anonymity, decentralization, emission, and reliability. Disadvantages include lack of guarantees, instability, risk of prohibition and possible loss. A model to forecast bitcoin cryptocurrency exchange rate is proposed and substantiated mathematically. The observations are the basis to develop a hypothesis: the faster the cryptocurrency (e.g. bitcoin) is mined, the more difficult it is to forecast bitcoin cryptocurrency exchange rate. Certain factors emphasize which may affect the cryptocurrency exchange rate: trade wars of the USA with other export-oriented world economies (China); advent of IEO as the alternative for ICO and new drivers connected with the interest in cryptocurrency; FATF recommendations for market control, the entry of world giants (e.g. Facebook) into the crypto-market and the development of the stablecoin market.
This paper explores the symmetric and asymmetric dependency structure of decomposed return series of Gold and eight cryptocurrencies to establish the hedging and diversification potentials of these asset classes. Daily data spanning 30 April 2013 to 18 April 2019 are employed within the Ensemble Empirical Mode Decomposition and Quantile-in-Quantile regression techniques. Our empirical results provide evidence that cryptocurrencies and Gold can both hedge and diversify for each other at different conditional distributions of their returns. We also find that cryptocurrencies are not purely speculative but can be driven by medium- and long-term fundamentals. In addition, both Gold and cryptocurrencies can be hedge and diversifiers for other traditional asset classes such as crude oil, fiat currencies, and other commodities.
This paper investigates the existence of herding behavior in cryptocurrencies market. Using data of the 20 large cryptocurrencies and MV Index Solution Crypto Compare Digital Assets for large cap index, we found no evidence of herding behavior using cross-sectional absolute standard deviation estimation. However, by applying a rolling window analysis, the results show significant herding behavior, which varies over time. Finally, we find an inverse relationship between herding behavior and the Bloomberg consumer comfort index which means that when traders are less comfortable they prefer to ignore their expectations and follow the market performance.
This paper documents a persistent structure in cryptocurrency returns and analyzes a broad set of characteristics that explain this structure. The results show that similarities in size, trading volume, age, consensus mechanism, and token industries drive the structure of cryptocurrency returns. But the highest variation is explained by a "connectivity" measure that proxies for similarity in cryptocurrencies' investor bases using their trading location. Currencies connected to other currencies that perform well generate sizably higher returns than the cross-section both contemporaneously and in the future. I examine three potential channels for these results. First, evidence from new exchange listings and a quasi-natural experiment shows that unobservable characteristics cannot explain the effect of connectivity. Second, decomposition of the order flows suggests that connectivity captures strong exchange-specific commonalities in crypto investors' demand that also spills over to other exchanges. Finally, analysis of social media data suggests that these demand shocks are a first order driver of cryptocurrency returns, largely because they can be perceived as a sign of user adoption.
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.
Abstract This paper proposes an empirical model for analysing the dynamics of Bitcoin prices. To do this, we consider a vector error correction model over two overlapping periods: 2010–17 and 2010–19. Price discovery is achieved through the Gonzalo–Granger permanent‐transitory decomposition. The pricing factors are endogenous linear combinations of the S&P 500 index, gold price, a Google search variable associated to Bitcoin and a fear index proxied by the FED Financial Stress Index. Our empirical analysis shows that during the first period, a linear combination of four pricing factors describes the efficient Bitcoin price. The S&P 500 index and Google searches have a positive effect whereas gold prices and the fear index have a negative effect. In contrast, during the second period, the efficient price behaves idiosyncratically and can be only rationalised by individuals' search for information on the cryptocurrency. These findings provide empirical evidence on the presence of a correction in Bitcoin prices during the period 2018–19 uncorrelated to market fundamentals. We also show that standard empirical asset pricing models perform poorly for explaining Bitcoin prices.