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.
We empirically study the state of three prominent DAO governance systems on the Ethereum blockchain: Compound, Uniswap and ENS. In particular, we examine how the voting power is distributed in these systems. Using a comprehensive dataset of all governance token holders, delegates, proposals and votes, we analyze who holds the voting power and how this power is being used to influence governance decisions. While we reveal that the majority of voting power is concentrated in the hands of a small number of addresses, we rarely observe these powerful entities overturning a vote by choosing a different outcome than that of the overall community and less influential voters.
Johannes Brustle, Paul Dütting, Balasubramanian Sivan
First-price auctions have many desirable properties, including uniquely possessing some, like credibility. However, first-price auctions are also inherently non-truthful, and non-truthfulness may result in instability and inefficiencies. Given these pros and cons, we seek to quantify the extent to which first-price auctions are susceptible to manipulation. In this work we adopt a metric that was introduced in the context of bitcoin fee design markets: the percentage change in payment that can be achieved by being strategic. We study the behavior of this metric for single-unit and $k$-unit auction environments with $n$ i.i.d. buyers, and seek conditions under which the percentage change tends to zero as $n$ grows large. To the best of our knowledge, ours is the first rigorous study of the extent to which large multi-unit first price auctions are susceptible to manipulation. We provide an almost complete picture of the conditions under which they are truthful in the large, and exhibit some surprising boundaries.
Cryptocurrencies are poised to revolutionize the modern economy by democratizing commerce. These currencies operate on top of blockchain-based distributed ledgers. Existing permissionless blockchain-based protocols offer unparalleled benefits like decentralization, anonymity, and transparency. However, these protocols suffer in performance which hinders their widespread adoption. In particular, high time-to-finality and low transaction rates keep them from replacing centralized payment systems such as the Visa network. Permissioned blockchain protocols offer attractive performance guarantees, but they are not considered suitable for deploying decentralized cryptocurrencies due to their centralized nature. Researchers have developed several multi-layered blockchain protocols that combine both permissioned and permissionless blockchain protocols to achieve high performance along with decentralization. The key idea with existing layered blockchain protocols in literature is to divide blockchain operations into two layers and use different types of blockchain protocols to manage each layer. However, many such works come with the assumptions of honest majority which may not accurately reflect the real world where the participants may be self-interested or rational. These assumptions may render the protocols susceptible to security threats in the real world, as highlighted by the literature focused on exploring game-theoretic attacks on these protocols. We generalize the "layered" approach taken by existing protocols in the literature and present a framework to analyze the system in the BAR Model and provide a generalized game-theoretic analysis of such protocols. Using our analysis, we identify the critical system parameters required for a distributed ledger's secure operation in a more realistic setting.
Hashed Timelock Contract (HTLC) in Lightning Network is susceptible to a griefing attack. An attacker can block several channels and stall payments by mounting this attack. A state-of-the-art countermeasure, Hashed Timelock Contract with Griefing-Penalty (HTLC-GP) is found to work under the classical assumption of participants being either honest or malicious but fails for rational participants. To address the gap, we introduce a game-theoretic model for analyzing griefing attacks in HTLC. We use this model to analyze griefing attacks in HTLC-GP and conjecture that it is impossible to design an efficient protocol that will penalize a malicious participant with the current Bitcoin scripting system. We study the impact of the penalty on the cost of mounting the attack and observe that HTLC-GP is weakly effective in disincentivizing the attacker in certain conditions. To further increase the cost of attack, we introduce the concept of \emph{guaranteed minimum compensation}, denoted as $ζ$, and modify HTLC-GP into $\textrm{HTLC-GP}^ζ$. By experimenting on several instances of Lightning Network, we observe that the total coins locked in the network drops to $28\%$ for $\textrm{HTLC-GP}^ζ$, unlike in HTLC-GP where total coins locked does not drop below $40\%$. These results justify that $\textrm{HTLC-GP}^ζ$ is better than HTLC-GP to counter griefing attacks.
One of the most innovative aspects of blockchain technology consists in the introduction of an incentive layer to regulate the behavior of distributed protocols. The designer of a blockchain system faces therefore issues that are akin to those relevant for the design of economic mechanisms, and faces them in a computational setting. From this perspective the present paper argues for the importance of computational social choice in blockchain research. It identifies a few challenges at the interface of the two fields that illustrate the strong potential for cross-fertilization between them.
Hans Gersbach, Akaki Mamageishvili, Manvir Schneider
On several proof-of-stake blockchains, agents engaged in validating transactions can open a pool to which others can delegate their stake in order to earn higher returns. We develop a model of staking pool formation in the presence of malicious agents and establish existence and uniqueness of equilibria. We then identify potential and risk of staking pools. First, allowing for staking pools lowers blockchain security. Yet, honest stake holders obtain higher returns. Second, by choosing welfare optimal distribution rewards, staking pools prevent that malicious agents receive large rewards. Third, when pool owners can freely distribute the returns from validation to delegators, staking pools disrupt blockchain operations, since malicious agents attract most delegators by offering generous returns.
Blockchain technology truly opened the gate to a wave of unparalleled innovations; however, despite the rapidly growing load of hype, the integration into the business, apart from a few applications, seems to be coming at a slower rate. One reason for that delay may be the need in the real-world applications for the so-called trust model. Trust models are rarely mentioned in blockchain application proposals despite their importance, which creates skepticism about their successful developments. To promote trust model implementation and help practitioners in its redaction, this article provides an outline of what a trust model is, why it is essential, and an example of how it is elaborated. The discussed example comes from a case study of a dairy company that implemented blockchain for the traceability of its products. Despite being tailored on a traceability project, the redaction and elements of the trust model, with few adjustments, could be easily readapted for other applications.
Blockchain front-running involves multiple agents, other than the legitimate agent, claiming a payment from performing a contract. It arises because of the public nature of blockchain transactions and potential network congestion. This paper notes that disputes over payments are similar to classic ownership disputes (such as King Solomon's dilemma). We propose a simultaneous report mechanism that resolves Solomon's dilemma (using only ordinal preference information) and also eliminates blockchain front-running. In each case, the mechanism relies on threats to remove ownership from all claimants and preferences from the legitimate claimant over allocations to other agents.
Blockchain-based federated learning (BCFL) has recently gained tremendous attention because of its advantages, such as decentralization and privacy protection of raw data. However, there has been few studies focusing on the allocation of resources for the participated devices (i.e., clients) in the BCFL system. Especially, in the BCFL framework where the FL clients are also the blockchain miners, clients have to train the local models, broadcast the trained model updates to the blockchain network, and then perform mining to generate new blocks. Since each client has a limited amount of computing resources, the problem of allocating computing resources to training and mining needs to be carefully addressed. In this paper, we design an incentive mechanism to help the model owner (MO) (i.e., the BCFL task publisher) assign each client appropriate rewards for training and mining, and then the client will determine the amount of computing power to allocate for each subtask based on these rewards using the two-stage Stackelberg game. After analyzing the utilities of the MO and clients, we transform the game model into two optimization problems, which are sequentially solved to derive the optimal strategies for both the MO and clients. Further, considering the fact that local training related information of each client may not be known by others, we extend the game model with analytical solutions to the incomplete information scenario. Extensive experimental results demonstrate the validity of our proposed schemes.
The selfish mining attack, arguably the most famous game-theoretic attack in blockchain, indicates that the Bitcoin protocol is not incentive-compatible. Most subsequent works mainly focus on strengthening the selfish mining strategy, thus enabling a single strategic agent more likely to deviate. In sharp contrast, little attention has been paid to the resistant behavior against the selfish mining attack, let alone further equilibrium analysis for miners and mining pools in the blockchain as a multi-agent system. In this paper, first, we propose a strategy called insightful mining to counteract selfish mining. By infiltrating an undercover miner into the selfish pool, the insightful pool could acquire the number of its hidden blocks. We prove that, with this extra insight, the utility of the insightful pool could be strictly greater than the selfish pool's when they have the same mining power. Then we investigate the mining game where all pools can either choose to be honest or take the insightful mining strategy. We characterize the Nash equilibrium of this mining game, and derive three corollaries: (a) each mining game has a pure Nash equilibrium; (b) honest mining is a Nash equilibrium if the largest mining pool has a fraction of mining power no more than 1/3; (c) there are at most two insightful pools under equilibrium no matter how the mining power is distributed.
Georgios Birmpas, Lyudmila Kovalchuk, Philip Lazos, Roman Oliynykov
Motivated by the intricacies of allocating treasury funds in blockchain settings, we study the problem of crowdsourcing reviews for many different proposals, in parallel. During the reviewing phase, every reviewer can select the proposals to write reviews for, as well as the quality of each review. The quality levels follow certain very coarse community guidelines and can have values such as 'excellent' or 'good'. Based on these scores and the distribution of reviews, every reviewer will receive some reward for their efforts. In this paper, we design a reward scheme and show that it always has pure Nash equilibria, for any set of proposals and reviewers. In addition, we show that these equilibria guarantee constant factor approximations for two natural metrics: the total quality of all reviews, as well as the fraction of proposals that received at least one review, compared to the optimal outcome.
Predatory trading bots lurking in Ethereum's mempool present invisible taxation of traders on automated market makers (AMMs). AMM traders specify a slippage tolerance to indicate the maximum price movement they are willing to accept. This way, traders avoid automatic transaction failure in case of small price movements before their trade request executes. However, while a too-small slippage tolerance may lead to trade failures, a too-large slippage tolerance allows predatory trading bots to profit from sandwich attacks. These bots can extract the difference between the slippage tolerance and the actual price movement as profit. In this work, we introduce the sandwich game to analyze sandwich attacks analytically from both the attacker and victim perspectives. Moreover, we provide a simple and highly effective algorithm that traders can use to set the slippage tolerance. We unveil that most broadcasted transactions can avoid sandwich attacks while simultaneously only experiencing a low risk of transaction failure. Thereby, we demonstrate that a constant auto-slippage cannot adjust to varying trade sizes and pool characteristics. Our algorithm outperforms the constant auto-slippage suggested by the biggest AMM, Uniswap, in all performed tests. Specifically, our algorithm repeatedly demonstrates a cost reduction exceeding a factor of 100.
The quality of learning generally improves with the scale and diversity of data. Companies and institutions can therefore benefit from building models over shared data. Many cloud and blockchain platforms, as well as government initiatives, are interested in providing this type of service. These cooperative efforts face a challenge, which we call ``exclusivity attacks''. A firm can share distorted data, so that it learns the best model fit, but is also able to mislead others. We study protocols for long-term interactions and their vulnerability to these attacks, in particular for regression and clustering tasks. We conclude that the choice of protocol, as well as the number of Sybil identities an attacker may control, is material to vulnerability.
Tiantian Gong, Ryan Henry, Alexandros Psomas, Aniket Kate
A long line of research on secure computation has confirmed that anything that can be computed, can be computed securely using a set of non-colluding parties. Indeed, this non-collusion assumption makes a number of problems solvable, as well as reduces overheads and bypasses computational hardness results, and it is pervasive across different privacy-enhancing technologies. However, it remains highly susceptible to covert, undetectable collusion among computing parties. This work stems from an observation that if the number of available computing parties is much higher than the number of parties required to perform a secure computation task, collusion attempts in privacy-preserving computations could be deterred. We focus on the prominent privacy-preserving computation task of multi-server $1$-private information retrieval (PIR) that inherently assumes no pair-wise collusion. For PIR application scenarios, such as those for blockchain light clients, where the available servers can be plentiful, a single server's deviating action is not tremendously beneficial to itself. We can make deviations undesired via small amounts of rewards and penalties, thus significantly raising the bar for collusion resistance. We design and implement a collusion mitigation mechanism on a public bulletin board with payment execution functions, considering only rational and malicious parties with no honest non-colluding servers. Privacy protection is offered for an extended period after the query executions.
Blockchain systems come with a promise of decentralization that often stumbles on a roadblock when key decisions about modifying the software codebase need to be made. This is attested by the fact that both of the two major cryptocurrencies, Bitcoin and Ethereum, have undergone hard forks that resulted in the creation of alternative systems, creating confusion and opportunities for fraudulent activities. These events, and numerous others, underscore the importance of Blockchain governance, namely the set of processes that blockchain platforms utilize in order to perform decision-making and converge to a widely accepted direction for the system to evolve. While a rich topic of study in other areas, governance of blockchain platforms is lacking a well established set of methods and practices that are adopted industry wide. This makes the topic of blockchain governance a fertile domain for a thorough systematization that we undertake in this work. We start by distilling a comprehensive array of properties for sound governance systems drawn from academic sources as well as grey literature of election systems and blockchain white papers. These are divided into seven categories, confidentiality, verifiability, accountability, sustainability, Pareto efficiency, suffrage and liveness that capture the whole spectrum of desiderata of governance systems. We proceed to classify ten well-documented blockchain systems. While all properties are satisfied, even partially, by at least one system, no system that satisfies most of them. Our work lays out a foundation for assessing blockchain governance processes. While it highlights shortcomings and deficiencies in currently deployed systems, it can also be a catalyst for improving these processes to the highest possible standard with appropriate trade-offs, something direly needed for blockchain platforms to operate effectively in the long term.
We study a scenario where an adjudication task (e.g., the resolution of a binary dispute) is outsourced to a set of agents who are appointed as jurors. This scenario is particularly relevant in a Web3 environment, where no verification of the adjudication outcome is possible, and the appointed agents are, in principle, indifferent to the final verdict. We consider simple adjudication mechanisms that use (1) majority voting to decide the final verdict and (2) a payment function to reward the agents with the majority vote and possibly punish the ones in the minority. Agents interact with such a mechanism strategically: they exert some effort to understand how to properly judge the dispute and cast a yes/no vote that depends on this understanding and on information they have about the rest of the votes. Eventually, they vote so that their utility (i.e., their payment from the mechanism minus the cost due to their effort) is maximized. Under reasonable assumptions about how an agent's effort is related to her understanding of the dispute, we show that appropriate payment functions can be used to recover the correct adjudication outcome with high probability. Our findings follow from a detailed analysis of the induced strategic game and make use of both theoretical arguments and simulation experiments.
What happens to mining when the Bitcoin price changes, when there are mining supply shocks, the price of energy changes, or hardware technology evolves? We give precise answers based on the technical forces and incentives in the system. We then build on these dynamics to consider value: what is the cost and purpose of mining, and is it worth it? Does it use too much energy, is it bad for the environment? Finally we extend our analysis to the long term: is mining economically feasible forever? What will the global hash rate be in 40 years? How is mining impacted by the limits of computation and energy? Is it physically sustainable in the long run? From first principles, we derive a fundamental scale-invariant feasibility constraint, which enables us to analyze the interlocking dynamics, find key invariants, and answer these questions mathematically.
A transaction fee mechanism (TFM) is an essential component of a blockchain protocol. However, a systematic evaluation of the real-world impact of TFMs is still absent. Using rich data from the Ethereum blockchain, the mempool, and exchanges, we study the effect of EIP-1559, one of the earliest-deployed TFMs that depart from the traditional first-price auction paradigm. We conduct a rigorous and comprehensive empirical study to examine its causal effect on blockchain transaction fee dynamics, transaction waiting times, and consensus security. Our results show that EIP-1559 improves the user experience by mitigating intrablock differences in the gas price paid and reducing users' waiting times. However, EIP-1559 has only a small effect on gas fee levels and consensus security. In addition, we find that when Ether's price is more volatile, the waiting time is significantly higher. We also verify that a larger block size increases the presence of siblings. These findings suggest new directions for improving TFMs.
Dimitris Karakostas, Aggelos Kiayias, Thomas Zacharias
We study Nash-dynamics in the context of blockchain protocols. We introduce a formal model, within which one can assess whether the Nash dynamics can lead utility-maximizing participants to defect from the "honest" protocol operation, towards variations that exhibit one or more undesirable infractions, such as abstaining from participation and producing conflicting protocol histories. Blockchain protocols that do not lead to such infraction states are said to be compliant. Armed with this model, we evaluate the compliance of various Proof-of-Work (PoW) and Proof-of-Stake (PoS) protocol families, with respect to different utility functions and reward schemes, leading to the following results: i) PoS ledgers under resource-proportional rewards can be compliant if costs are negligible, but non-compliant if costs are significant; ii) PoW and PoS under block-proportional rewards exhibit different compliance behavior, depending on the lossiness of the network; iii) PoS ledgers can be compliant w.r.t. one infraction, i.e., producing conflicting messages, but non-compliant (and non-equilibria) w.r.t. abstaining or an attack we call selfish signing; iv) taking externalities, such as exchange rate fluctuations, into account, we quantify the benefit of economic penalties, in the context of PoS protocols, in disincentivizing particular infractions.
Decentralized Finance (DeFi) is an emerging financial service model based on blockchain technology. DeFi composability denotes the ability for different DeFi services to interact with one another resulting in new forms of financial services. The DeFi ecosystem is largely based on ERC-20 tokens that can represent the value of an asset. Collateralized assets in DeFi composability are locked and additional profit cannot be generated. In this paper, we propose a method to generate profit from locked assets by using ERC-721 Non-Fungible Tokens (NFTs) and perpetual contracts. NFT represents the rights to a certain asset. A perpetual contract is a futures contract that does not have an expiration date. We propose perpetual contract NFT, a new form of NFT that can be used as collateral, which exploits perpetual futures contracts in the cryptocurrency derivatives market. Collateral needs to be provided to back the value of a perpetual contract. If the perpetual contract is minted as NFT, the resulting NFT represents the rights to the perpetual contract and its collateral. Therefore, the perpetual contact NFT itself can be used as collateral for DeFi composability. A proof-of-concept smart contract and a web application for perpetual contract NFT are provided to demonstrate its functionality. To validate the profitability of the perpetual contract NFT using a real-world scenario, we experiment with the position NFT of Uniswap v3 decentralized exchange. The position NFT is a form of perpetual contract NFT. Specifically, we present validation with three types of pools: stablecoins, stablecoin/wrapped tokens pair, and wrapped tokens.
The Non-Fungible Token (NFT) is viewed as one of the important applications of blockchain technology. Although NFT has a large market scale and multiple practical standards, several limitations of the existing mechanism in NFT markets exist. This work proposes a novel securitization and repurchase scheme for NFT to overcome these limitations. We first provide an Asset-Backed Securities (ABS) solution to settle the limitations of non-fungibility of NFT. Our securitization design aims to enhance the liquidity of NFTs and enable Oracles and Automatic Market Makers (AMMs) for NFTs. Then we propose a novel repurchase protocol for a participant owing a portion of NFT to repurchase other shares to obtain the complete ownership. As participants may strategically bid during the acquisition process, our repurchase process is formulated as a Stackelberg game to explore the equilibrium prices. We also provide solutions to handle difficulties at market such as budget constraints and lazy bidders.
This research contributes to the security design of an advanced smart drone swarm network based on a variant of the Blockchain Governance Game (BGG), which is the theoretical game model to predict the moments of security actions before attacks, and the Strategic Alliance for Blockchain Governance Game (SABGG), which is one of the BGG variants which has been adapted to construct the best strategies to take preliminary actions based on strategic alliance for protecting smart drones in a blockchain-based swarm network. Smart drones are artificial intelligence (AI)-enabled drones which are capable of being operated autonomously without having any command center. Analytically tractable solutions from the SABGG allow us to estimate the moments of taking preliminary actions by delivering the optimal accountability of drones for preventing attacks. This advanced secured swarm network within AI-enabled drones is designed by adapting the SABGG model. This research helps users to develop a new network-architecture-level security of a smart drone swarm which is based on a decentralized network.
This paper studies a fundamental problem regarding the security of blockchain PoW consensus on how the existence of multiple misbehaving miners influences the profitability of selfish mining. Each selfish miner (or attacker interchangeably) maintains a private chain and makes it public opportunistically for acquiring more rewards incommensurate to his Hash power. We first establish a general Markov chain model to characterize the state transition of public and private chains for Basic Selfish Mining (BSM), and derive the stationary profitable threshold of Hash power in closed-form. It reduces from 25% for a single attacker to below 21.48% for two symmetric attackers theoretically, and further reduces to around 10% with eight symmetric attackers experimentally. We next explore the profitable threshold when one of the attackers performs strategic mining based on Partially Observable Markov Decision Process (POMDP) that only half of the attributes pertinent to a mining state are observable to him. An online algorithm is presented to compute the nearly optimal policy efficiently despite the large state space and high dimensional belief space. The strategic attacker mines selfishly and more agilely than BSM attacker when his Hash power is relatively high, and mines honestly otherwise, thus leading to a much lower profitable threshold. Last, we formulate a simple model of absolute mining revenue that yields an interesting observation: selfish mining is never profitable at the first difficulty adjustment period, but replying on the reimbursement of stationary selfish mining gains in the future periods. The delay till being profitable of an attacker increases with the decrease of his Hash power, making blockchain miners more cautious on performing selfish mining.