Mining attacks allow adversaries to obtain a disproportionate share of the mining reward by deviating from the honest mining strategy in the Bitcoin system. Among them, the most well-known are selfish mining (SM), block withholding (BWH), fork after withholding (FAW) and bribery mining. In this paper, we propose two novel mining attacks: bribery semi-selfish mining (BSSM) and bribery stubborn mining (BSM). Both of them can increase the relative extra reward of the adversary and will make the target bribery miners suffer from the bribery miner dilemma. All targets earn less under the Nash equilibrium. For each target, their local optimal strategy is to accept the bribes. However, they will suffer losses, comparing with denying the bribes. Furthermore, for all targets, their global optimal strategy is to deny the bribes. Quantitative analysis and simulation have been verified our theoretical analysis. We propose practical measures to mitigate more advanced mining attack strategies based on bribery mining, and provide new ideas for addressing bribery mining attacks in the future. However, how to completely and effectively prevent these attacks is still needed on further research.
Stanisław Drożdż, Jarosław Kwapień, Marcin Wątorek
In relation to the traditional financial markets, the cryptocurrency market is a recent invention and the trading dynamics of all its components are readily recorded and stored. This fact opens up a unique opportunity to follow the multidimensional trajectory of its development since inception up to the present time. Several main characteristics commonly recognized as financial stylized facts of mature markets were quantitatively studied here. In particular, it is shown that the return distributions, volatility clustering effects, and even temporal multifractal correlations for a few highest-capitalization cryptocurrencies largely follow those of the well-established financial markets. The smaller cryptocurrencies are somewhat deficient in this regard, however. They are also not as highly cross-correlated among themselves and with other financial markets as the large cryptocurrencies. Quite generally, the volume V impact on price changes R appears to be much stronger on the cryptocurrency market than in the mature stock markets, and scales as $R(V) \sim V^α$ with $α\gtrsim 1$.
Blockchain enables peer-to-peer transactions in cyberspace without a trusted third party. The rapid growth of Ethereum and smart contract blockchains generally calls for well-designed Transaction Fee Mechanisms (TFMs) to allocate limited storage and computation resources. However, existing research on TFMs must consider the waiting time for transactions, which is essential for computer security and economic efficiency. Integrating data from the Ethereum blockchain and memory pool (mempool), we explore how two types of events affect transaction latency. First, we apply regression discontinuity design (RDD) to study the causal inference of the Merge, the most recent significant upgrade of Ethereum. Our results show that the Merge significantly reduces the long waiting time, network loads, and market congestion. In addition, we verify our results' robustness by inspecting other compounding factors, such as censorship and unobserved delays of transactions via private changes. Second, examining three major protocol changes during the merge, we identify block interval shortening as the most plausible cause for our empirical results. Furthermore, in a mathematical model, we show block interval as a unique mechanism design choice for EIP1559 TFM to achieve better security and efficiency, generally applicable to the market congestion caused by demand surges. Finally, we apply time series analysis to research the interaction of Non-Fungible token (NFT) drops and market congestion using Facebook Prophet, an open-source algorithm for generating time-series models. Our study identified NFT drops as a unique source of market congestion -- holiday effects -- beyond trend and season effects. Finally, we envision three future research directions of TFM.
Manal Mansour, May Salama, Hala Helmi, Mona F. M. Mursi
Blockchain technology is referred to as a very secure decentralized, distributed ledger that records the history of any digital asset. It is being used in numerous governmental and private sector organizations across numerous nations. Surveying the current state of blockchain applications and difficulties in e-government services is the goal of this review. Held to the account are use cases for current facilities that use blockchain. Finally, it examines the research gap in blockchain deployment and makes suggestions for future work for additional research.
A non-fungible token (NFT) is a data unit stored on the blockchain. Nowadays, more and more investors and collectors (NFT traders), who participate in transactions of NFTs, have an urgent need to assess the performance of NFTs. However, there are two challenges for NFT traders when analyzing the performance of NFT. First, the current rarity models have flaws and are sometimes not convincing. In addition, NFT performance is dependent on multiple factors, such as images (high-dimensional data), history transactions (network), and market evolution (time series). It is difficult to take comprehensive consideration and analyze NFT performance efficiently. To address these challenges, we propose NFTVis, a visual analysis system that facilitates assessing individual NFT performance. A new NFT rarity model is proposed to quantify NFTs with images. Four well-coordinated views are designed to represent the various factors affecting the performance of the NFT. Finally, we evaluate the usefulness and effectiveness of our system using two case studies and user studies.
Decentralized finance (DeFi) is an integral component of the blockchain ecosystem, enabling a range of financial activities through smart-contract-based protocols. Traditional DeFi governance typically involves manual parameter adjustments by protocol teams or token holder votes, and is thus prone to human bias and financial risks, undermining the system's integrity and security. While existing efforts aim to establish more adaptive parameter adjustment schemes, there remains a need for a governance model that is both more efficient and resilient to significant market manipulations. In this paper, we introduce "Auto$.$gov", a learning-based governance framework that employs a deep Qnetwork (DQN) reinforcement learning (RL) strategy to perform semi-automated, data-driven parameter adjustments. We create a DeFi environment with an encoded action-state space akin to the Aave lending protocol for simulation and testing purposes, where Auto$.$gov has demonstrated the capability to retain funds that would have otherwise been lost to price oracle attacks. In tests with real-world data, Auto$.$gov outperforms the benchmark approaches by at least 14% and the static baseline model by tenfold, in terms of the preset performance metric--protocol profitability. Overall, the comprehensive evaluations confirm that Auto$.$gov is more efficient and effective than traditional governance methods, thereby enhancing the security, profitability, and ultimately, the sustainability of DeFi protocols.
Liquidations in Decentralized Finance (DeFi) are both a blessing and a curse -- whereas liquidations prevent lenders from capital loss, they simultaneously lead to liquidation spirals and system-wide failures. Since most lending and borrowing protocols assume liquidations are indispensable, there is an increased interest in alternative constructions that prevent immediate systemic-failure under uncertain circumstances. In this work, we introduce reversible call options, a novel financial primitive that enables the seller of a call option to terminate it before maturity. We apply reversible call options to lending in DeFi and devise Miqado, a protocol for lending platforms to replace the liquidation mechanisms. To the best of our knowledge, Miqado is the first protocol that actively mitigates liquidations to reduce the risk of liquidation spirals. Instead of selling collateral, Miqado incentivizes external entities, so-called supporters, to top-up a borrowing position and grant the borrower additional time to rescue the debt. Our simulation shows that Miqado reduces the amount of liquidated collateral by 89.82% in a worst-case scenario.
Decentralized Exchanges (DEXs) are new types of marketplaces leveraging Blockchain technology. They allow users to trade assets with Automatic Market Makers (AMM), using funds provided by liquidity providers, removing the need for order books. One such DEX, Uniswap v3, allows liquidity providers to allocate funds more efficiently by specifying an active price interval for their funds. This introduces the problem of finding an optimal strategy for choosing price intervals. We formalize this problem as an online learning problem with non-stochastic rewards. We use regret-minimization methods to show a liquidity provision strategy that guarantees a lower bound on the reward. This is true even for non-stochastic changes to asset pricing, and we express this bound in terms of the trading volume.
Justin Goldston, Tomer Jordi Chaffer, Justyna Osowska, Charles von Goins
In recent years discussions centered around digital inheritance have increased among social media users and across blockchain ecosystems. As a result digital assets such as social media content cryptocurrencies and non-fungible tokens have become increasingly valuable and widespread, leading to the need for clear and secure mechanisms for transferring these assets upon the testators death or incapacitation. This study proposes a framework for digital inheritance using soulbound tokens and the social recovery pallet as a use case in the Polkadot and Kusama blockchain networks. The findings discussed within this study suggest that while soulbound tokens and the social recovery pallet offer a promising solution for creating a digital inheritance plan the findings also raise important considerations for testators digital executors and developers. While further research is needed to fully understand the potential impacts and risks of other technologies such as artificial intelligence and quantum computing this study provides a primer for users to begin planning a digital inheritance strategy and for developers to develop a more intuitive solution.
The primary innovation we pioneer -- focused on blockchain information security -- is called the Safe-House. The Safe-House is badly needed since there are many ongoing hacks and security concerns in the DeFi space right now. The Safe-House is a piece of engineering sophistication that utilizes existing blockchain principles to bring about greater security when customer assets are moved around. The Safe-House logic is easily implemented as smart contracts on any decentralized system. The amount of funds at risk from both internal and external parties -- and hence the maximum one time loss -- is guaranteed to stay within the specified limits based on cryptographic fundamentals. To improve the safety of the Safe-House even further, we adapt the one time password (OPT) concept to operate using blockchain technology. Well suited to blockchain cryptographic nuances, our secondary advancement can be termed the one time next time password (OTNTP) mechanism. The OTNTP is designed to complement the Safe-House making it even more safe. We provide a detailed threat assessment model -- discussing the risks faced by DeFi protocols and the specific risks that apply to blockchain fund management -- and give technical arguments regarding how these threats can be overcome in a robust manner. We discuss how the Safe-House can participate with other external yield generation protocols in a secure way. We provide reasons for why the Safe-House increases safety without sacrificing the efficiency of operation. We start with a high level intuitive description of the landscape, the corresponding problems and our solutions. We then supplement this overview with detailed discussions including the corresponding mathematical formulations and pointers for technological implementation. This approach ensures that the article is accessible to a broad audience.
In the era of digital markets, the challenge for consumers is discerning quality amidst information asymmetry . While traditional markets use brand mechanisms to address this issue, transferring such systems to internet-based P2P markets – where misleading practices like fake ratings are rampant – remains challenging. Current internet platforms strive to counter this through verification algorithms, but these efforts find themselves in a continuous tug-of-war with counterfeit actions. Exploiting the transparency, immutability, and traceability of blockchain technology, this paper introduces a robust reputation voting system grounded in it. Unlike existing blockchain-based reputation systems, our model harnesses an intrinsically economically incentivized approach to bolster agent integrity. We optimize this model to mirror real-world user behavior , preserving the reputation system’s foundational sustainability . Through Monte-Carlo simulations, using both uniform and power-law distributions enabled by an innovative inverse transform method, we traverse a broad parameter landscape, replicating real-world complexity. The findings underscore the promise of a sustainable, transparent, and formidable reputation mechanism. Given its structure, our framework can potentially function as a universal, sustainable oracle for offchain-onchain bridging, aiding entities in perpetually cultivating their reputation. Future integration with technologies like Ring Signature and Zero Knowledge Proof could amplify the system’s privacy facets, rendering it particularly influential in the ever-evolving digital domain.
The limit order book mechanism has been the core trading mechanism of the modern financial market. In the cryptocurrency market, centralized exchanges also adopt this limit order book mechanism and a centralized matching engine dynamically connects the traders to the orders of market makers. Recently, decentralized exchanges have been introduced and received considerable attention in the cryptocurrency community. A decentralized exchange typically adopts an automated market maker, which algorithmically arbitrates the trades between liquidity providers and traders through a pool of crypto assets. Meanwhile, the liquidity of the exchange is the most important factor when traders choose an exchange. However, the amount of liquidity provided by the liquidity providers in decentralized exchanges is insufficient when compared to centralized exchanges. This is because the liquidity providers in decentralized exchanges suffer from the risk of divergence loss inherent to the automated market making system. To this end, we introduce a new concept called margin liquidity and leverage this concept to propose a highly profitable margin liquidity-providing position. Then, we extend this margin liquidity-providing position to a virtual margin liquidity-providing position to alleviate the risk of divergence loss for the liquidity providers and encourage them to provide more liquidity to the pool. Furthermore, we introduce a representative strategy for the margin liquidity-providing position and backtest the strategy with historical data from the BTC/ETH market. Our strategy outperforms a simple holding baseline. We also show that our proposed margin liquidity is 8K times more capital efficient than the concentrated liquidity proposed in Uniswap V3.
A. A. Talha Talukder, Md. Anisul Islam Mahmud, Arbiya Sultana, Tahmid Hasan Pranto · 6 authors
Food delivery systems are gaining popularity recently due to the expansion of internet connectivity and for the increasing availability of devices. The growing popularity of such systems has raised concerns regarding (i) Information security, (ii) Business to business (B2B) deep discounting race, and (iii) Strict policy enforcement. Sensitive personal data and financial information of the users must be safeguarded. Additionally, in pursuit of gaining profit, the restaurants tend to offer deep discounts resulting in a higher volume of orders than usual. Therefore, the restaurants and the delivery persons fail to maintain the delivery time and often impair the food quality. In this paper, we have proposed a blockchain and smart contract-based food delivery system to address these issues. The main goal is to remove commission schemes and decrease service delays caused by a high volume of orders. The protocols have been deployed and tested on the Ethereum test network. The simulation manifests a successful implementation of our desired system; with the payment being controlled by our system. The actors (restaurant, delivery-person or consumer) are bound to be compliant with the policies or penalized otherwise.
Lioba Heimbach, Eric Schertenleib, Roger Wattenhofer
Financial markets have evolved over centuries, and exchanges have converged to rely on the order book mechanism for market making. Latency on the blockchain, however, has prevented decentralized exchanges (DEXes) from utilizing the order book mechanism and instead gave rise to the development of market designs that are better suited to a blockchain. Although the first widely popularized DEX, Uniswap V2, stood out through its astonishing simplicity, a recent design overhaul introduced with Uniswap V3 has introduced increasing levels of complexity aiming to increase capital efficiency. In this work, we empirically study the ability of Unsiwap V3 to handle unexpected price shocks. Our analysis finds that the prices on Uniswap V3 were inaccurate during the recent abrupt price drops of two stablecoins: UST and USDT. We identify the lack of agility required of Unsiwap V3 liquidity providers as the root cause of these worrying price inaccuracies. Additionally, we outline that there are too few incentives for liquidity providers to enter liquidity pools, given the elevated volatility in such market conditions.
JoaquÃn Delgado Fernández, Tom Barbereau, Orestis Papageorgiou
With advancements in distributed ledger technologies and smart contracts, tokenized voting rights gained prominence within decentralized finance (DeFi). Voting rights tokens (a.k.a. governance tokens) are fungible tokens that grant individual holders the right to vote upon the fate of a project. The motivation behind these tokens is to achieve decentral control within a decentralized autonomous organization (DAO). Because the initial allocations of these tokens is often undemocratic, the DeFi project and DAO of Yearn Finance experimented with a fair launch allocation where no tokens are pre-mined and all participants have an equal opportunity to receive them. Regardless, research on voting rights tokens highlights the formation of timocracies over time. The consideration is that the tokens’ tradability is the cause of concentration. To examine this proposition, this article uses an agent-based model to simulate and analyze the concentration of voting rights tokens post three fair launch allocation scenarios under different trading modalities. The results show that regardless of the allocation, concentration persistently occurs. It confirms the consideration that the ‘disease’ is endogenous: the cause of concentration is the tokens’ tradability. The findings inform theoretical understandings and practical implications for on-chain governance mediated by tokens.
We are often faced with the non-trivial task of designing incentive mechanisms in the era of Web3. As history has shown, many Web3 services failed mostly due to the lack of a rigorous incentive mechanism design based on token economics. However, traditional mechanism design, where there is an assumption that the users of services strategically make decisions so that their expected profits are maximized, often does not capture their real behavior well as it ignores humans' psychological bias in making decisions under uncertainty. In this paper, we propose an incentive mechanism design for crypto-enabled services using behavioral economics. Specifically, we take an example of a crypto lottery game in this work and incorporate a seminal work of cumulative prospect theory into its lottery game mechanism (or rule) design. We designed four mechanisms and compared them in terms of utility, a metric of how appealing a mechanism is to participants, and a game operator's expected profit. Our approach is generic and will be applicable to a wide range of crypto-based services where a decision has to be made under uncertainty.
Benford's law describes the distribution of the first digit of numbers appearing in a wide variety of numerical data, including tax records, and election outcomes, and has been used to raise "red flags" about potential anomalies in the data such as tax evasion. In this work, we ask the following novel question: given a large transaction or financial graph, how do we find a set of nodes that perform many transactions among each other that also deviate significantly from Benford's law? We propose the AntiBenford subgraph framework that is founded on well-established statistical principles. Furthermore, we design an efficient algorithm that finds AntiBenford subgraphs in near-linear time on real data. We evaluate our framework on both real and synthetic data against a variety of competitors. We show empirically that our proposed framework enables the detection of anomalous subgraphs in cryptocurrency transaction networks that go undetected by state-of-the-art graph-based anomaly detection methods. Our empirical findings show that our \ab framework is able to mine anomalous subgraphs, and provide novel insights into financial transaction data. The code and the datasets are available at \url{https://github.com/tsourakakis-lab/antibenford-subgraphs}.
Experience has shown that trading in stock and cryptocurrency markets has the potential to be highly profitable. In this light, considerable effort has been recently devoted to investigate how to apply machine learning and deep learning to interpret and predict market behavior. This research introduces a new deep neural network architecture and a novel idea of how to prepare financial data before feeding them to the model. In the data preparation part, the first step is to generate many features using technical indicators and then apply the XGBoost model for feature engineering. Splitting data into three categories and using separate autoencoders, we extract high-level mixed features at the second step. This data preprocessing is introduced to predict price movements. Regarding modeling, different convolutional layers, an long short-term memory unit, and several fully-connected layers have been designed to perform binary classification. This research also introduces a trading strategy to exploit the trained model outputs. Three different datasets are used to evaluate this method, where results indicate that this framework can provide us with profitable and robust predictions.
Vytautas Tumas, Sean Rivera, Damien Magoni, Radu State
XRP Ledger is one of the oldest, well-established blockchains. Despite the popularity of the XRP Ledger, little is known about its underlying peer-to-peer network. The structural properties of a network impact its efficiency, security and robustness. We aim to close the knowledge gap by providing a detailed analysis of the XRP overlay network. In this paper we examine the graph-theoretic properties of the XRP Ledger peer-to-peer network and its temporal characteristics. We crawl the XRP Ledger over two months and collect 1,290 unique network snapshots. We uncover a small group of nodes that act as a networking backbone. In addition, we observe a high network churn, with a third of the nodes changing every five days. Our findings have strong implications for the resilience and safety of the XRP Ledger.
In accordance with the guidelines of the Financial Action Task Force (FATF), Virtual Asset Service Providers (VASPs) should comply with a `travel rule', which requires them to exchange originator's and beneficiary's personal information when transferring virtual assets. In this paper, we propose a novel blockchain-based travel rule compliance system that supports fully-decentralized data exchange. The proposed system uses a permissioned blockchain, and thereby eliminates the possibility of leakage of personal information to third parties or even to travel rule service providers, and ensures that travel rule data can be managed securely.
Panoptic is the perpetual, oracle-free, instant-settlement options trading protocol on the Ethereum blockchain. Panoptic enables the permissionless trading of options on top of any asset pool in the Uniswap v3 ecosystem and seeks to develop a trustless, permissionless, and composable options product, i.e., do for decentralized options markets what x*y=k automated market maker protocols did for spot trading.
Decentralized applications developed using blockchain technology provide innovative business models to serve the human race and solve existing challenges. Climate change is one of the biggest problems humanity is facing and there is a dearth of solutions in tackling this grave impediment to the long-term sustainability of our planet. Accountability, greenwashing, traceability, impact assessment and trading of carbon credits are unresolved issues in the ESG sector. In this paper, we present a novel decentralized application software, DCarbonX, that solves the enumerated problems using NFTs on the blockchain platform, through smart contracts. The paper describes the functional architecture of DCarbonX, while elaborating on its salient features and utility in sustainable finance, in particular green sukuk. DCarbonX is a pioneering software providing an exchange for trading of carbon credits. The software facilitates logging of impact and traceable transactions in a carbon market, that would help to prevent duplication of records and greenwashing. The paper discusses the efforts being undertaken to achieve the climate goals as per the Paris Agreement and also highlights the pivotal obstacles to achieving carbon neutrality by 2050, as per COP26. The paper also encompasses a study on the applications of dapps in DeFi, Web 3.0 and ESG, among other areas and gives a comparative analysis of blockchain platforms for dapp development. The paper is also a pioneer in highlighting the challenges that plague dapp development, deployment and usage.
Jan Arvid Berg, Robin Fritsch, Lioba Heimbach, Roger Wattenhofer
Decentralized exchanges are revolutionizing finance. With their ever-growing increase in popularity, a natural question that begs to be asked is: how efficient are these new markets? We find that nearly 30% of analyzed trades are executed at an unfavorable rate. Additionally, we observe that, especially during the DeFi summer in 2020, price inaccuracies across the market plagued DEXes. Uniswap and SushiSwap, however, quickly adapt to their increased volumes. We see an increase in market efficiency with time during the observation period. Nonetheless, the DEXes still struggle to track the reference market when cryptocurrency prices are highly volatile. During such periods of high volatility, we observe the market becoming less efficient - manifested by an increased prevalence in cyclic arbitrage opportunities.
Mohammad Arafat Ali Khan, Chandra Bhushan, Vadlamani Ravi, Vangala Sarveswara Rao · 5 authors
This paper proposes nowcasting of high-frequency financial datasets in real-time with a 5-minute interval using the streaming analytics feature of Apache Spark. The proposed 2 stage method consists of modelling chaos in the first stage and then using a sliding window approach for training with machine learning algorithms namely Lasso Regression, Ridge Regression, Generalised Linear Model, Gradient Boosting Tree and Random Forest available in the MLLib of Apache Spark in the second stage. For testing the effectiveness of the proposed methodology, 3 different datasets, of which two are stock markets namely National Stock Exchange & Bombay Stock Exchange, and finally One Bitcoin-INR conversion dataset. For evaluating the proposed methodology, we used metrics such as Symmetric Mean Absolute Percentage Error, Directional Symmetry, and Theil U Coefficient. We tested the significance of each pair of models using the Diebold Mariano (DM) test.