The market for cryptocurrencies is interspersed with cases of loss, theft and fraud and a new transnational practice in bankruptcy law is emerging whereby cryptocurrency exchanges compensate the injured users on a collective basis. This paper will argue: first, that this trend has transplanted into Asia and Europe the US idea according to which bankruptcy law can be employed to avoid mass litigation; secondly, that this trend has transcended the debate about the characterization of digital assets, including the concerns of those scholars who maintain that digital coins cannot be objects of property; and thirdly that – since this practice follows the pattern of so-called restorative justice and since cryptocurrencies are highly volatile – injured users, as creditors of the exchanges, ought to be satisfied in kind, i.e. incryptocurrencies themselves.
The largest and best-known Cryptocurrency in the global economy is Bitcoin but it is only one of approximately 2,000 cyptocurrencies in circulation today. A Bitcoin was worth 8,790.51 U.S. dollars as of March 4, 2020 and all the Bitcoins in the world were worth roughly $160.4 billion. In Bitcoin you don’t need to explicitly register or reveal your real world identity, but the pattern of your behavior might itself be identifying. This is the fundamental privacy question in a Cryptocurrency like Bitcoin leading to emergence of Privacy coins such a Monero and Zcash that utilize complex cryptography to achieve the greater Privacy levels with more anonymity features. However, in the zest for increased Privacy with focus on anonymity in Cryptocurrency transaction, the law and order issues are compromised wherein it would be near to impossible for the law enforcement agencies to track criminals dealing in money laundering, terrorist financing, tax evasion and other frauds by using Crypto currencies. In this background, it is necessary to understand “Cryptocurrency and Privacy” as an interface to better comprehends the subject of Cyptocurrency which is very dynamic in technology with multiple global implications on the economic and legal front. The present work aims to study Cryptocurrency in the context of Privacy. The foundational concepts and definitions of the two competing subjects: ‘Cryptocurrency and Privacy’ is taken up for better understanding the background of the interface. Tor, an anonymous communication network is referred in brief to state that the dilemma in the Privacy context is not unique to use of Crypto currencies technology in so far its negative effects are concerned . The conclusion suggest for finding an appropriate balance between an individual’s privacy and State’s security in Cryptocurrency technology.
This paper analyses co-movement between Bitcoin exchanges in 34 major countries around the world and the US (the global benchmark) over the period January 24, 2011 - January 7, 2019. More specifically, we run IV regressions to investigate the importance of cultural factors (such as tightness, individualism, trust and risk-taking) following an earlier study by Eun et al. (2015) which had shed light on their importance to explain stock co-movement within individual countries. The results suggest that markets in tighter, more individualistic, trustful and risk-taking societies are more tightly linked to the US one. Further, it appears that culturally looser, collectivistic, trustful and risk-taking countries are more likely to shut down their Bitcoin exchanges compared to other countries. These findings confirm our priors.
Bank for International Settlements, Raphael Auer, Stijn Claessens, Bank for International Settlements
Cryptocurrencies are often thought to operate out of the reach of national regulation, but in fact their valuations, transaction volumes and user bases react substantially to news about regulatory actions. The impact depends on the specific regulatory category to which the news relates: events related to general bans on cryptocurrencies or to their treatment under securities law have the greatest adverse effect, followed by news on combating money laundering and the financing of terrorism, and on restricting the interoperability of cryptocurrencies with regulated markets. News pointing to the establishment of specific legal frameworks tailored to cryptocurrencies and initial coin offerings coincides with strong market gains. These results suggest that cryptocurrency markets rely on regulated financial institutions to operate and that these markets are segmented across jurisdictions.
We assess how the cost structure of cryptocurrency mining affects the response of miners to exchange rate fluctuations and the immutability of cryptocurrency ledgers that rely on proof-of-work. We show that the amount of mining power supplied to currencies that rely on specialized hardware, such as Bitcoin, responds less to adverse exchange rate shocks than other currencies respond to such shocks, a fact that is instrumental to avoiding double-spending attacks. The results may change if mining equipment used for one cryptocurrency can be transferred to another. For smaller currencies with low exchange rate correlation, transferability eliminates the protection that fixed costs provide. Our results weaken doomsday predictions for Bitcoin and other cryptocurrencies with declining block rewards. This paper was accepted by Bruno Biais, Special Section of Management Science: Blockchains and Crypto Economics. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4901 .
Being the most popular permissionless blockchain that supports smart contracts, Ethereum allows any user to create accounts on it. However, not all accounts matter. For example, the accounts due to attacks can be removed. In this paper, we conduct the first investigation on erasable accounts that can be removed to save system resources and even users' money (i.e., ETH or gas). In particular, we propose and develop a novel tool named GLASER, which analyzes the State DataBase of Ethereum to discover five kinds of erasable accounts. The experimental results show that GLASER can accurately reveal 508,482 erasable accounts and these accounts lead to users wasting more than 106 million dollars. GLASER can help stop further economic loss caused by these detected accounts. Moreover, GLASER characterizes the attacks/behaviors related to detected erasable accounts through graph analysis.
Are price discontinuities in cryptocurrencies jointly related to large swings in geopolitical risk? This is a relevant question to answer given recent news from the press that Bitcoin’s price jumps are driven by jumps in the level of geopolitical risk index. To answer this question, we examine first the jump incidence of daily returns for Bitcoin and other leading cryptocurrencies and then study the co-jumps between cryptocurrencies and the geopolitical risk index using logistic regressions. Our dataset is at the daily frequency and covers the period 30 April 2013 to 31 October 2019. The results show that the price behaviour of all cryptocurrencies under study is jumpy but only Bitcoin jumps are dependent on jumps in the geopolitical risk index. This revealed evidence of significant co-jumps for the case of Bitcoin only nicely complements previous studies arguing that Bitcoin is a hedge against geopolitical risk.
ABSTRACT The meteoric growth of global cryptocurrency markets presents novel challenges to regulators. Some policymakers and scholars warn that regulation will cause trading activity to cross borders into less-regulated jurisdictions—or even smother a promising new financial asset class. Others believe regulatory actions will stimulate activity by providing clarity to market participants. Standing behind this disagreement is a debate about the desirability of either outcome. Some believe that governments should promote development of the cryptocurrency sector within their countries, while others view cryptocurrencies as conduits of illegality and fraud that should be restricted through strict regulation or even outright bans. Yet these debates have, to date, been conducted almost entirely without data concerning the effects of regulation on market activity. As a corrective, in this article we assembled original data on cryptocurrency regulations worldwide and used them to empirically examine movement in trading activity at a number of exchanges following key regulatory announcements. We found that a wide variety of models yielded almost entirely null results. From the creation of bespoke licensing regimes to targeted anti-money-laundering and anti-fraud enforcement actions, as well as many other categories of government activities, we found no systemic evidence that regulatory measures cause traders to flee, or enter into, the affected jurisdictions. These findings at last provide an empirical basis for regulatory decisions concerning cryptocurrency trading. Among other things, they call into question that capital flight or chilling effects should be a first-order concern.
The Covid-19 bear market presents the first acute market losses since active trading of Bitcoin began. This market downturn provides a timely test of the frequently expounded safe haven properties of Bitcoin. In this paper, we show that Bitcoin does not act as a safe haven, instead decreasing in price in lockstep with the S&P 500 as the crisis develops. When held alongside the S&P 500, even a small allocation to Bitcoin substantially increases portfolio downside risk. Our empirical findings cast doubt on the ability of Bitcoin to provide shelter from turbulence in traditional markets.
Bitcoin is by far the most popular crypto-currency solution enabling peer-to-peer payments. Despite some studies highlighting the network does not provide full anonymity, it is still being heavily used for a wide variety of dubious financial activities such as money laundering, ponzi schemes, and ransom-ware payments. In this paper, we explore the landscape of potential money laundering activities occurring across the Bitcoin network. Using data collected over three years, we create transaction graphs and provide an in-depth analysis on various graph characteristics to differentiate money laundering transactions from regular transactions. We found that the main difference between laundering and regular transactions lies in their output values and neighbourhood information. Then, we propose and evaluate a set of classifiers based on four types of graph features: immediate neighbours, curated features, deepwalk embeddings, and node2vec embeddings to classify money laundering and regular transactions. Results show that the node2vec-based classifier outperforms other classifiers in binary classification reaching an average accuracy of 92.29% and an F1-measure of 0.93 and high robustness over a 2.5-year time span. Finally, we demonstrate how effective our classifiers are in discovering unknown laundering services. The classifier performance dropped compared to binary classification, however, the prediction can be improved with simple ensemble techniques for some services.
Agreements, consensuses, protocols, resource-sharing, and fairness are all examples of social and political metaphors that define and shape new computational algorithms. The thought experiments and allegories about resource-sharing or agreement between nodes played a vital role in the development of "concurrent programming" (enabling processor power-sharing and process synchronization) and still later in the development of distributed computing (facilitating data access and synchronization). These paved the way for current concepts of consensus mechanisms, smart contracts, and other descriptions of cryptocurrencies, blockchain, distributed ledger, and hashgraph technologies, paradoxically reversing the relations between metaphor and artifact. New computing concepts and algorithmic processes, such as consensus mechanisms, trustless networks, and automated smart contracts or DAOs (Distributed Autonomous Organizations), aim to disrupt social contracts and political decision-making and replace economic, social, and political institutions (e.g., law, money, voting). Rather than something that needs a metaphor, algorithms are becoming the metaphor of good governance. Current fantasies of algorithmic governance exemplify this reversal of the role played by metaphors: they reduce all concepts of governance to automation and curtail opportunities for defining new computing challenges inspired by the original allegories, thought experiments, and metaphors. Especially now, when we are still learning how best to govern the transgressions and excesses of emerging distributed ledger technologies, productive relations between software and allegory, algorithms and metaphors, code and law are possible so long as they remain transitive. Against this tyranny of algorithms and technologies as metaphors and aspirational models of governance, we propose sandboxes and environments that allow stakeholders to combine prototyping with deliberation, algorithms with metaphors, codes with regulations.
Andrea Jonathan Pagano, Francesco Romagnoli, Emanuele Vannucci
Abstract Risk insurance for disasters plays a relevant part in the implementation of risk reduction strategies during the pre-disaster phase. This is essential to support risk management towards decreasing the marginal risk allowing policy holders to transfer risk to avoid considerable financial loads from the costs incurred during the recovery phase in a post-disaster phase. There is evidence that the introduction of an integrated risk insurance strategy for community resilience planning is still lacking. Thus, this undermines the possibility to have proper optimized holistic risk management; on the one hand this strengthens pre-disaster risk mitigation measures, mostly relying on mitigative infrastructural solutions, and on the other hand it better defines risk prevention strategies mostly connected to land planning and urban development. This paper will show how insurance markets can play a key role towards mitigating the economic consequences of natural and climate change disasters, and how essential it is to better quantify the beneficial effects and costs of engineer-based mitigative solutions. In this context, the legal framework into which the actuarial quantitative model can be implemented will support the creation of an integrated multidisciplinary approach with potential implementation on a novel platform capable of collecting and processing information from different sources and dimensions such as blockchain technology. The scientific community is, in fact, increasingly interested in implementing blockchain technology to overcome problems linked to the contractual dimension of natural disaster risk insurance which can be interpreted as a sort of smart contracting. Through a study that involved four distinct areas, namely: law, environmental engineering, insurance and IT, this paper proposes a specific multidisciplinary methodology to achieve the drafting and implementation of a digital insurance contract on a blockchain platform against natural hazards. This paper proposes the basis to advance a quantitative concept to optimize the impact of catastrophe risk insurance onto the community resilience; in fact providing a key synergy for definition of pre-disaster conditions.
Executing, verifying and enforcing credible transactions on permissionless blockchains is done using smart contracts. A key challenge with smart contracts is ensuring their correctness and security. To address this challenge, we present a fully automated technique, SolAnalyser, for vulnerability detection over Solidity smart contracts that uses both static and dynamic analysis. Analysis techniques in the literature rely on static analysis with a high rate of false positives or lack support for vulnerabilities like out of gas, unchecked send, timestamp dependency. Our tool, SolAnalyser, supports automated detection of 8 different vulnerability types that currently lack wide support in existing tools, and can easily be extended to support other types. We also implemented a fault seeding tool that injects different types of vulnerabilities in smart contracts. We use the mutated contracts for assessing the effectiveness of different analysis tools. Our experiment uses 1838 real contracts from which we generate 12866 mutated contracts by artificially seeding 8 different vulnerability types. We evaluate the effectiveness of our technique in revealing the seeded vulnerabilities and compare against five existing popular analysis tools - Oyente, Securify, Maian, SmartCheck and Mythril. This is the first large scale evaluation of existing tools that compares their effectiveness by running them on a common set of contracts. We find that our technique outperforms all five existing tools in supporting detection of all 8 vulnerability types and in achieving higher precision and recall rate. SolAnalyser was also faster in analysing the different vulnerabilities than any of the existing tools in our experiment.
Abeer ElBahrawy, Laura Alessandretti, Leonid Rusnac, Daniel Goldsmith · 6 authors
Dark markets are commercial websites that use Bitcoin to sell or broker transactions involving\ndrugs, weapons, and other illicit goods. Being illegal, they do not offer any user protection, and\nseveral police raids and scams have caused large losses to both customers and vendors over the past\nyears. However, this uncertainty has not prevented a steady growth of the dark market phenomenon\nand a proliferation of new markets. The origin of this resilience have remained unclear so far, also due\nto the difficulty of identifying relevant Bitcoin transaction data. Here, we investigate how the dark\nmarket ecosystem re-organises following the disappearance of a market, due to factors including raids\nand scams. To do so, we analyse 24 episodes of unexpected market closure through a novel datasets\nof 133 million Bitcoin transactions involving 31 dark markets and their users, totalling 4 billion USD.\nWe show that coordinated user migration from the closed market to coexisting markets guarantees\noverall systemic resilience beyond the intrinsic fragility of individual markets. The migration is\nswift, efficient and common to all market closures. We find that migrants are on average more active\nusers in comparison to non-migrants and move preferentially towards the coexisting market with\nthe highest trading volume. Our findings shed light on the resilience of the dark market ecosystem\nand we anticipate that they may inform future research on the self-organisation of emerging online\nmarkets.
Francesco Zola, Jan L. Bruse, Maria Eguimendia, Mikel Galar · 5 authors
The Bitcoin network not only is vulnerable to cyber-attacks but currently represents the most frequently used cryptocurrency for concealing illicit activities. Typically, Bitcoin activity is monitored by decreasing anonymity of its entities using machine learning-based techniques, which consider the whole blockchain. This entails two issues: first, it increases the complexity of the analysis requiring higher efforts and, second, it may hide network micro-dynamics important for detecting short-term changes in entity behavioral patterns. The aim of this paper is to address both issues by performing a “temporal dissection” of the Bitcoin blockchain, i.e., dividing it into smaller temporal batches to achieve entity classification. The idea is that a machine learning model trained on a certain time-interval (batch) should achieve good classification performance when tested on another batch if entity behavioral patterns are similar. We apply cascading machine learning principles—a type of ensemble learning applying stacking techniques—introducing a “k-fold cross-testing” concept across batches of varying size. Results show that blockchain batch size used for entity classification could be reduced for certain classes (Exchange, Gambling, and eWallet) as classification rates did not vary significantly with batch size; suggesting that behavioral patterns did not change significantly over time. Mixer and Market class detection, however, can be negatively affected. A deeper analysis of Mining Pool behavior showed that models trained on recent data perform better than models trained on older data, suggesting that “typical” Mining Pool behavior may be represented better by recent data. This work provides a first step towards uncovering entity behavioral changes via temporal dissection of blockchain data.