Cryptoaltruism refers to the ways in which distributed ledger technologies, especially blockchains, are changing the nature of the nonprofit sector. This study specifically investigates how the blockchain technology has been used by Ukrainian nonprofits during the current Russia-Ukraine War. To link this to the more general literature on blockchains, we consider whether blockchains are used primarily as a general-purpose technology or as an institutional technology which redefines how nonprofits coordinate activities. Our analysis of Ukrainian nonprofits provides evidence supporting both perspectives. Widespread acceptance of cryptocurrency suggests blockchains are an efficiency-enhancing new technology. We also show that novel applications on nonfungible tokens to preserve art and culture and to raise funds, as well as uses of blockchains to address challenges with trust, lend support to the idea of blockchains as an innovative institutional technology that is transforming the nature of the nonprofit sector. This study intends to motivate further development of the emergent agenda on cryptoaltruism and its role in the nonprofit sector.
Abstract Since its inception in 2009, Bitcoin has become and is currently the most successful and widely used cryptocurrency. It introduced blockchain technology, which allows transactions that transfer funds between users to take place online, in an immutable manner. No real-world identities are needed or stored in the blockchain. At the same time, all transactions are publicly available and auditable, making Bitcoin a pseudo-anonymous ledger of transactions. The volume of transactions that are broadcast on a daily basis is considerably large. We propose a set of features that can be extracted from transaction data. Using this, we apply a data processing pipeline to ultimately cluster transactions via a k-means clustering algorithm, according to the transaction properties. Finally, according to these properties, we are able to characterize these clusters and the transactions they include. Our work mainly differentiates from previous studies in that it applies an unsupervised learning method to cluster transactions instead of addresses. Using the novel features we introduce, our work classifies transactions in multiple clusters, while previous studies only attempt binary classification. Results indicate that most transactions fall into a cluster that can be described as common user transactions. Other clusters include transactions made by online exchanges and lending services, those relating to mining activities as well as smaller clusters, one of which contains possibly illicit or fraudulent transactions. We evaluated our results against an online database of addresses that belong to known actors, such as online exchanges, and found that our results generally agree with them, which enhances the validity of our methods.
The evolving landscape of Decentralized Finance (DeFi) has raised critical security concerns, especially pertaining to Protocols for Loanable Funds (PLFs) and their dependency on price oracles, which are susceptible to manipulation. The emergence of flash loans has further amplified these risks, enabling increasingly complex oracle manipulation attacks that can lead to significant financial losses. Responding to this threat, we first dissect the attack mechanism by formalizing the standard operational and adversary models for PLFs. Based on our analysis, we propose SecPLF, a robust and practical solution designed to counteract oracle manipulation attacks efficiently. SecPLF operates by tracking a price state for each crypto-asset, including the recent price and the timestamp of its last update. By imposing price constraints on the price oracle usage, SecPLF ensures a PLF only engages a price oracle if the last recorded price falls within a defined threshold, thereby negating the profitability of potential attacks. Our evaluation based on historical market data confirms SecPLF's efficacy in providing high-confidence prevention against arbitrage attacks that arise due to minor price differences. SecPLF delivers proactive protection against oracle manipulation attacks, offering ease of implementation, oracle-agnostic property, and resource and cost efficiency.
Anton Wahrstätter, Alfred Taudes, Davor Svetinović
Privacy within the Bitcoin ecosystem has been critical for the operation and propagation of the system since its very first release. While various entities have sought to deanonymize and reveal user identities, the default semi-anonymous approach to privacy was judged as insufficient and the community developed a number of advanced privacy-preservation mechanisms. In this study, we propose an improved variant of the multiple-input clustering approach that incorporates advanced privacy-enhancing techniques. We examine the CoinJoin-adjusted user graph of Bitcoin through quantitative network analysis and draw conclusions on the effectiveness of our proposed clustering method compared to naive multiple-input clustering. Our findings indicate that CoinJoin transactions can significantly distort commonly applied address clustering approaches. Moreover, we demonstrate that Bitcoin's user graph has become less dense in recent years, concurrent with the collapse of several independent user clusters. Our results contribute to a more comprehensive understanding of privacy aspects in the Bitcoin transaction network and lay the groundwork for developing enhanced measures to prevent money laundering and terrorism financing.
Abstract Identifying illicit behavior in the Bitcoin network is a well‐explored topic. The methods proposed over time have generated great insights into the deanonymization of the Bitcoin user base through the clustering of inputs and outputs. With advanced techniques being deployed by Bitcoin users, these heuristics are now being challenged in their ability to aid in the detection of illicit activity. In this paper, we provide a comprehensive list of methods deployed by malicious actors on the network and illicit transaction mining methods. We detail the evolution of the heuristics that are used to deanonymize Bitcoin transactions. We highlight the issues associated with conducting law enforcement investigations and propose recommendations for the research community to address these issues. Our recommendations include the release of public data by exchanges to allow researchers and law enforcement to further protect the network from malicious users. We recommend the enhancement of current heuristics through machine learning methods and discuss how researchers can take the fight head‐on against expert cybercriminals.
The exploitation of smart contract vulnerabilities in Decentralized Finance (DeFi) has resulted in financial losses exceeding 3 billion US dollars. Existing defense mechanisms primarily focus on detecting and reacting to adversarial transactions executed by attackers that target victim contracts. However, with the emergence of private transaction pools where transactions are sent directly to miners without first appearing in public mempools, current detection tools face significant challenges in identifying attack activities effectively. Based on the fact that most attack logic rely on deploying intermediate smart contracts as supporting components to the exploitation of victim contracts, novel detection methods have been proposed that focus on identifying these adversarial contracts instead of adversarial transactions. However, previous state-of-the-art approaches in this direction have failed to produce results satisfactory enough for real-world deployment. In this paper, we propose LookAhead, a new framework for detecting DeFi attacks via unveiling adversarial contracts. LookAhead leverages common attack patterns, code semantics and intrinsic characteristics found in adversarial smart contracts to train Machine Learning (ML)-based classifiers that can effectively distinguish adversarial contracts from benign ones and make timely predictions of different types of potential attacks. Experiments on our labeled datasets show that LookAhead achieves an F1-score as high as 0.8966, which represents an improvement of over 44.4% compared to the previous state-of-the-art solution, with a False Positive Rate (FPR) at only 0.16%.
Purpose The study provides a comprehensive understanding of the issues and illegal activities related to cryptocurrencies and their negative repercussions. This study aims to identify and classify cryptocurrency downsides using grounded theory and in-depth interviews. The study also analysed investors’ reluctance to invest in cryptocurrency. This pioneering qualitative study illuminates a deep and multifaceted criminal aspect of cryptocurrency. Design/methodology/approach The study conducted in-depth interviews with respondents who have experience and knowledge of cryptocurrency investments. The interviews were recorded and transcribed. The analysis was performed using the NVivo 14 software in the study. Findings The study specified two major types of cryptocurrency’s negative aspects: barriers and illegal usage. Barriers to cryptocurrency investment include technological, security, trust, market-related and regulatory reasons. Terrorist funding, money laundering, fraud and ransom payments are all examples of illegal usage. The results of the word cloud analysis are consistent with the overall findings of the survey, which highlighted illegal usage as a prominent negative element of cryptocurrencies. It is a key reason why cryptocurrency is not included in investing portfolios by investors. Originality/value The study’s findings provide useful insights for policymakers to develop better methods for successfully mitigating risks and ensuring responsible and sustainable usage of cryptocurrencies. In addition, the study could serve as a stepping stone for more cryptocurrency-related studies, contributing to the development of a more complete and nuanced comprehension of this emergent technology and its societal effects.
There is a distinct lack of criminological research examining victimisation experiences in emerging cryptocurrency frauds. At the same time, online cryptocurrency communities have become a key part of the social milieu of the cryptocurrency ecosystem where scams are commonplace. Using Reddit forum data from the subreddit r/ CryptoCurrency, this exploratory qualitative study investigates how users in an online cryptocurrency community share knowledge and experiences of cryptocurrency scams. Thematic analysis revealed how online cryptocurrency communities discuss scams by (1) arming the community (e.g. newcomer guides, personal disclosures of scam victimisation, and reflections on the technological affordances in scams); and (2) establishing community norms in response to cryptocurrency scams (e.g. protecting the community, ‘scambaiting’ practices, normalising scams as an outcome of ‘decentralisation’). Gaining a deeper understanding of cryptocurrency scam experiences provides timely insights into the intersections between victims/offenders in digital environments, how we can respond to the recent growth in cryptocurrency scams, and the variegated ways that victims seek assistance following experiences.
Money laundering in cryptocurrencies is a significant concern, as it facilitates and conceals crime and can distort markets and the broader financial system. To combat this issue, researchers have turned to techniques to develop effective Anti-Money Laundering (AML) frameworks. The findings contribute to the ongoing efforts to promote social good by reducing the impact of criminal activities on society. By preventing money laundering, we can also help to combat other criminal activities such as drug trafficking, corruption, and terrorism. This paper focuses on the use of Graph Neural Networks (GNNs) to classify cryptocurrencies transactions. Specifically, the study employs Graph Convolutional Networks (GCNs), Graph Attention Networks (GAT), the Chebyshev spatial convolutional neural network (ChebNet), and GraphSAGE network to classify Bitcoin transactions. The study finds that ChebNet, GraphSAGE and a variant of GAT outperform other methods and improve upon the state of the art in terms of recall and F1 scores, thus suggesting that they can be more reliable in identifying illicit transactions.
Vulnerabilities in smart contracts may trigger serious security events, and the detection of smart contract vulnerabilities has become a significant problem. In this paper, to solve the limitations of current deep learning-based vulnerability detection methods in extracting various code critical features, using the multi-scale cascade encoder architecture as the backbone, we propose a novel Multi-Scale Encoder Vulnerability Detection (MEVD) approach to hit well-known high-risk vulnerabilities in smart contracts. Firstly, we use the gating mechanism to design a unique Surface Feature Encoder (SFE) to enrich the semantic information of code features. Then, by combining a Base Transformer Encoder (BTE) and a Detail CNN Encoder (DCE), we introduce a dual-branch encoder to capture the global structure and local detail features of the smart contract code, respectively. Finally, to focus the model’s attention on vulnerability-related characteristics, we employ the Deep Residual Shrinkage Network (DRSN). Experimental results on three types of high-risk vulnerability datasets demonstrate performance compared to state-of-the-art methods, and our method achieves an average detection accuracy of 90%.
Deflation represents an increase in consumer wealth through postponed consumption decisions. State-imposed "monetary policies" not only expropriate this increase in wealth attributed to deflation (a fiscal motive) but also penalize consumers for postponing their consumption of goods and services (a form of social engineering). Consequently, there is a shortfall in the state's legal frameworks adequately protecting consumer freedom and property rights. In response, the Bitcoin network has emerged as a private currency governed by a distinct legal framework rooted in proof-of-work. Consumers holding Bitcoin benefit from the economic advantages of global deflation-advantages often usurped by central banks-and experience enhanced freedom to delay consumption, navigating their life paths free from the constraints of social engineering. Thus, Bitcoin contributes value by addressing the shortfall in state legal systems that safeguard consumer freedom and property rights
Cryptocurrency tracker is an online platform that provides a userfriendly experience. Users get a simple and userfriendly experien ce through the user interface. Users can sign into their account with Gmail or a mobile number for easy access to their account. U sers can track prices of different cryptocurrencies and view currency charts. Using this user interface, users can find prices and ot her relevant information about cryptocurrencies. The app helps users to create watchlists and we can track prices. We can set alerts for cryptocurrency prices. We can customize notifications and help understand new cryptocurrency trends. Users can easily find various cryptocurrencies and track future crypt currency trends. It helps users invest in new popular cryptocurrencies that will be more useful to them in the future. Overall, the Cryptocurrency Tracker web app is a valuable tool for anyone looking to invest, trade, or just keep an eye on the cryptocurrency market. It provides realtime data and insights that can help users make informed investment decisions and stay abrea st of the latest industry trends and developments.
The tendency to transform individual social communities into a universal, unified society which knows no boundaries (or at least does not insist on them), with the necessary dose of simplification, is the conceptual definition of globalization. Since it is a tendency, globalization naturally has its own temporal dimension. The process of determining the time coordinates of this phenomenon is extremely complex and the results may vary depending on the scientific approach and point of view. However, we believe that it is possible to reach a consensus that the process of globalization is not continuous, but has several phases. We tend to think that there are three main phases of globalization. Of course, we fully respect other competing systems of periodization and argumentation on which they are based. Nevertheless, we do not doubt that proponents of different periodization would agree that a new phase of globalization began recently, although it is difficult to determine the exact moment when it was initiated. Namely, on October 31, 2009, a mysterious developer, or more likely group of developers, behind the fictious name of Satoshi Nakamoto, published the so-called White Paper titled Bitcoin: A Peer-to-Peer Electronic Cash System. More than ten years later, on March 11, 2020, the World Health Organization declared the outbreak of SARS-CoV-2 pandemic. These two, at first glance unrelated events cumulatively initiated chain of new transformations, all leading toward a more unified society. Probably the most important change, one that is the focus of this paper, is the transformation of centralized localized monetary systems into completely decentralized, digitalized, totally independent, almost entirely self-sustaining, and self-regulating global financial structure. This paper presents a part of the results collected through the theoretical-empirical research conducted previously on both legal and socio-economic moments that initiated a new phase of globalization. These results refer mostly to events that initiated a new phase of globalization, their connections, and problems of periodization. The remaining results, results on the scope and expected overall effects of this phase, will be presented in the next article in this series. All the results of the research and conclusions were collected based on different analytical-synthetical methods, mostly abstraction and generalization. In this paper specifically, different techniques of the normative method were used. Also, special techniques of legal norm interpretation were used in the process.