Atsuki Koyama, Kentaroh Toyoda, Manato Fujimoto, Thi Hong Tran
The rapid advancement of deepfake technology poses serious risks, including financial fraud and political misinformation, demanding robust methods for verifying image content authenticity. While the C2PA standard and zero-knowledgeproof-based methods provide an image content authenticity proving mechanism, the existing solutions struggle to efficiently support privacy-preserving edits and iterative modifications. To address these challenges, we propose zk-REAL (Zero-Knowledge-Based Protocol for Repeated Image Edit Authenticity Proof with Lattice Hashing), a framework that leverages a lightweight lattice-based hashing scheme within a zero-knowledge proof system. Our approach significantly reduces computational overhead, enabling faster proof generation and smaller proof size even for high-resolution images. Additionally, the updatability of our hashing method supports iterative edits, such as mosaicking or partial modifications, by minimizing redundant computations. Finally, to ensure compatibility with the C2PA ecosystem and conventional signature verifications, we integrate SHA-256 outside of the zero-knowledge circuit. Our evaluation shows up to a 29% reduction in computational costs for proof generation, showcasing the potential of zk-REAL in practical content authenticity verification scenarios.
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Advanced Steganography and Watermarking Techniques
Jims Marchang, R. Srikanth, Solan Keishing, Indranee Kashyap
Traditional blockchain consensus mechanisms, such as Proof of Work (PoW) and Proof of Stake (PoS), face significant challenges related to the centralisation of validators and miners, environmental impact, and trustworthiness. While PoW is highly secure, it is energy-intensive, and PoS tends to favour wealthy stakeholders, leading to validator centralisation. Existing mechanisms lack fairness, and the aspect of sustainability is not considered. Moreover, it fails to address social trust dynamics within validator selection. To bridge this research gap, this paper proposes Proof of Friendship (PoF)—a novel consensus mechanism that leverages social trust by improving decentralisation, enhancing fairness and sustainability among the validators. Unlike traditional methods that rely solely on computational power or financial stakes, PoF integrates friendship-based trust scores with geo-location diversity, transaction reliability, and sustainable energy adoption. By incorporating a trust graph, where validators are selected based on their verified relationships within the network, PoF mitigates the risks of Sybil attacks, promotes community-driven decentralisation, and enhances the resilience of the blockchain against adversarial manipulation. This research introduces the formal model of PoF, evaluates its security, decentralisation, and sustainability trade-offs, and demonstrates its effectiveness compared to existing consensus mechanisms. Our investigation and results indicate that PoF achieves higher decentralisation, improved trustworthiness, reduced validator monopolisation, and enhanced sustainability while maintaining strong network security. This study opens new avenues for socially aware blockchain governance, making consensus mechanisms more equitable, efficient, and environmentally responsible. This consensus mechanism demonstrates a holistic approach to modern blockchain design, addressing key challenges in trust, performance, and sustainability. The mechanism is tested theoretically and experimentally to validate its robustness and functionality. Processing latency (PL), network latency (NL) [transaction size/network speed], synchronisation delays (SDs), and cumulative delay per transaction are 85 ms, 172 ms, 1802 ms, [PL + NL + SD] 2059 ms, respectively.
The article was first published in English language by Fordham Law Review. For more information, please contact tmelnick@law.fordham.edu For original publication: Bobek, H. (2023). To mint or not to mint: non-fungible tokens and the right of publicity. Fordham Law Review, 92(2), 639. Publication URL: https://ir.lawnet.fordham.edu/flr/vol92/iss2/12 Objective: to study the legal regulation of NFT technology under the US legislation and to develop proposals to minimize offenses involving its use, including those related to violations of the right to publicity. Methods: dialectical approach to cognition of social phenomena, allowing to analyze them in historical development and functioning in the context of the totality of objective and subjective factors, which predetermined the following research methods formal-logical and sociological. Results: NFT technology creates new challenges in the field of protecting the right to publicity. Using NFT to violate the right to publicity creates serious difficulties for copyright holders seeking compensation for the damage caused. The difficulty of protecting the right to publicity in this market is due to the unique democratic nature of the token, the widespread anonymity of NFTs and the irreversibility of transactions with them, as well as the uncertainty of contractual terms with respect to secondary buyers. Scientific novelty: based on the analysis of judicial practice, the article examines the issues of violation of the right to publicity arising in connection with the NFT and possible approaches to this problem. Legal scholars and commentators argue that certain features of NFTs pose pronounced threats to the right of publicity, namely the technology’s novelty, democratized nature, anonymization of creators, transferability across platforms, and immutability. To combat these threats, the author proposes that rights owners should enter into right of publicity license agreements; that NFT platforms should strengthen their terms of service and develop higher barriers of entry for users; and, finally, that courts should order that infringing NFTs be “burned”. Practical significance: the main provisions and conclusions of the article can be used in scientific, pedagogical and law enforcement activities when considering the issues related to the legal regulation of NFT technology.
This study examines the representation and evaluation of Web3 technology in the German music media from 2016 to 2022, focusing on its prevalence, framing, and acceptance in the context of the music sector. Utilizing framing theory and the Technology Acceptance Model, a quantitative content analysis was conducted on articles from various music magazines. The findings indicate a generally positive portrayal of Web3, with significant discussion peaks in 2019, 2021, and 2022. Notably, no coverage was found in music education magazines, suggesting a gap in Web3 engagement in pedagogy. Much of the coverage was in Musikwoche, highlighting Web3’s impact on business aspects like ticketing, copyright, and licensing. The overall positive depiction, juxtaposed with limited critical evaluation, points to the need for a more nuanced discourse. The study underscores implications for balanced media coverage, informed musician engagement with Web3, and the potential for incorporating this technology in music education. It highlights the importance for the music industry of capitalizing on Web3’s positive aspects while practising critical awareness, and it calls for further research to explore the depth of Web3’s influence in music.
Pierpaolo Della Monica, Ivan Visconti, Andrea Vitaletti, Marco Zecchini
Before a fair exchange takes place, there is typically an advertisement phase with the goal of increasing the appeal of possessing a digital asset while keeping it sufficiently hidden. Advertisement phases are implicit in mainstream definitions, and therefore are not explicitly integrated within fair-exchange protocols. In this work we give an explicit definition for such a fair exchange in a setting where parties communicate via broadcast messages only (i.e., no point-to-point connection between seller and buyer is needed). Next, we construct a fair-exchange protocol satisfying our new definition using zk-SNARKs and relying on mainstream decentralized platforms (i.e., a blockchain with smart contracts like Ethereum and a decentralized storage system like IPFS). Experimental results confirm the practical relevance of our decentralized approach, paving the road towards building decentralized marketplaces where users can, even anonymously, and without direct off-chain communications, effectively advertise and exchange their digital assets as part of a system of enhanced NFTs.
Nenad Milošević, Daniel Cason, Zarko Milošević, Robert Soulé · 5 authors
Synchronous consensus protocols offer a significant advantage over their asynchronous and partially synchronous counterparts by providing higher fault tolerance -- an essential benefit in distributed systems, like blockchains, where participants may have incentives to act maliciously. However, despite this advantage, synchronous protocols are often met with skepticism due to concerns about their performance, as the latency of synchronous protocols is tightly linked to a conservative time bound for message delivery. This paper introduces AlterBFT, a new Byzantine fault-tolerant consensus protocol. The key idea behind AlterBFT lies in the new model we propose, called hybrid synchronous system model. The new model is inspired by empirical observations about network behavior in the public cloud environment and combines elements from the synchronous and partially synchronous models. Namely, it distinguishes between small messages that respect time bounds and large messages that may violate bounds but are eventually timely. Leveraging this observation, AlterBFT achieves up to 15$\times$ lower latency than state-of-the-art synchronous protocols while maintaining similar throughput and the same fault tolerance. Compared to partially synchronous protocols, AlterBFT provides higher fault tolerance, higher throughput, and comparable latency.
Following the publication of Bitcoin's arguably most famous attack, selfish mining, various works have introduced mechanisms to enhance blockchain systems' game theoretic resilience. Some reward mechanisms, like FruitChains, have been shown to be equilibria in theory. However, their guarantees assume non-realistic parameters and their performance degrades significantly in a practical deployment setting. In this work we introduce a reward allocation mechanism, called Proportional Splitting (PRS), which outperforms existing state of the art. We show that, for large enough parameters, PRS is an equilibrium, offering the same theoretical guarantees as the state of the art. In addition, for practical, realistically small, parameters, PRS outperforms all existing reward mechanisms across an array of metrics. We implement PRS on top of a variant of PoEM, a Proof-of-Work (PoW) protocol that enables a more accurate estimation of each party's mining power compared to e.g., Bitcoin. We then evaluate PRS both theoretically and in practice. On the theoretical side, we show that our protocol combined with PRS is an equilibrium and guarantees fairness, similar to FruitChains. In practice, we compare PRS with an array of existing reward mechanisms and show that, assuming an accurate estimation of the mining power distribution, it outperforms them across various well-established metrics. Finally, we realize this assumption by approximating the power distribution via low-work objects called "workshares" and quantify the tradeoff between the approximation's accuracy and storage overhead.
Money laundering is a financial crime that obscures the origin of illicit funds, necessitating the development and enforcement of anti-money laundering (AML) policies by governments and organizations. The proliferation of mobile payment platforms and smart IoT devices has significantly complicated AML investigations. As payment networks become more interconnected, there is an increasing need for efficient real-time detection to process large volumes of transaction data on heterogeneous payment systems by different operators such as digital currencies, cryptocurrencies and account-based payments. Most of these mobile payment networks are supported by connected devices, many of which are considered loT devices in the FinTech space that constantly generate data. Furthermore, the growing complexity and unpredictability of transaction patterns across these networks contribute to a higher incidence of false positives. While machine learning solutions have the potential to enhance detection efficiency, their application in AML faces unique challenges, such as addressing privacy concerns tied to sensitive financial data and managing the real-world constraint of limited data availability due to data regulations. Existing surveys in the AML literature broadly review machine learning approaches for money laundering detection, but they often lack an in-depth exploration of advanced deep learning techniques - an emerging field with significant potential. To address this gap, this paper conducts a comprehensive review of deep learning solutions and the challenges associated with their use in AML. Additionally, we propose a novel framework that applies the least-privilege principle by integrating machine learning techniques, codifying AML red flags, and employing account profiling to provide context for predictions and enable effective fraud detection under limited data availability....
The increasing adoption of blockchain technology has led to a surge in financial fraud, including money laundering, Ponzi schemes, and illicit fund transfers. Traditional fraud detection techniques, such as rule-based systems and supervised machine learning models, struggle to handle the high-volume, high-velocity, and dynamically evolving nature of blockchain transactions. These limitations necessitate a scalable and adaptive approach to detect fraudulent activities efficiently. This study introduces a Spatial-Temporal Graph Neural Network (STGNN)-based fraud detection framework, specifically designed for scalable anomaly detection in large-scale blockchain networks. By modeling blockchain transactions as a spatial-temporal graph, the proposed system captures structural dependencies between wallets and temporal patterns of fund movements. The STGNN model employs graph convolutional networks (GCN) or graph attention networks (GAT) for spatial feature extraction and gated recurrent units (GRU) or temporal convolutional networks (TCN) for sequential fraud pattern recognition. Additionally, to ensure scalability, the framework incorporates graph partitioning techniques, parallelized mini-batch training, and distributed processing, enabling real-time fraud detection across high-throughput blockchain networks. Extensive experiments conducted on Bitcoin and Ethereum transaction datasets demonstrate that the STGNN model achieves higher accuracy, lower false positive rates, and improved computational efficiency compared to rule-based fraud detection systems, supervised ML models, and static GNNs. Case studies further confirm the model’s effectiveness in detecting large-scale fraud schemes, such as DeFi exploits, cross-chain laundering, and coordinated illicit transactions. This research highlights the potential of graph-based deep learning techniques in blockchain security, providing a foundation for future advancements in scalable fraud detection, cross-chain anomaly detection, and decentralized financial security monitoring.
—Superfin is an integrated web application that com- bines cryptocurrency trading and e-wallet services into a single platform. It allows users to trade crypto, manage payments, access real-time news, and learn about blockchain in one place. The platform enhances user experience, strengthens financial security, and promotes crypto literacy. Built on a cloud-based architecture, it uses HTML5, CSS3, and JavaScript for the frontend, while PHP and MySQL handle secure transactions and real-time trading via APIs like Binance and TradingView. Security challenges are tackled with encryption and microser- vices. Superfin simplifies finance management, making crypto more accessible and secure for all users. Index Terms—Cryptocurrency Trading, E-Wallet Services, Fin- Tech Integration, Blockchain Technology, Decentralized Finance (DeFi), Real-Time Data Synchronization, User Experience (UX), API Integration, Financial Security, Digital Wallet, Cryptocur- rency News Aggregation, Learning Modules in Cryptocurrency
This study investigates the return propagation dynamics between cryptocurrencies and Emerging market sectoral indices (EMSI), focusing on portfolio impact from Bitcoin, Ethereum, and two gold-backed cryptocurrencies (PAXG and X8X). Using data from 2019 to 2024, we apply a novel DCC-GARCH-based R 2 decomposed connectedness approach to analyse return connectedness among these high-risk assets. We also utilize innovative concepts such as minimum dynamic pairwise connectedness and minimum R 2 decomposed connectedness portfolios in our multivariate hedging portfolios. Our findings reveal that total connectedness is time-variant and influenced by economic events. Bitcoin and Ethereum are identified as net transmitters of shocks, while other assets, particularly gold-backed cryptocurrencies, serve as net shock receivers with minimal impact. Moreover, few EMSIs (financials, industrials, and materials sectors) show significant connectedness in the system. Although our suggested portfolio analysis offers improved returns, none consistently outperform the market. This research offers valuable insights for investors and policymakers regarding the interconnectedness and risk management of cryptocurrencies and EMSI.
Ibrahim Garba Kabo, Georgina N. Obunadike, Nuruddeen A. Samaila
Bitcoin, the leading cryptocurrency, has gained significant attention due to its high volatility and potential economic impact. Traditional financial forecasting models struggle to accurately predict Bitcoin prices due to its sensitivity to various factors, including market sentiment and macroeconomic conditions. Existing models primarily rely on historical price data, often neglecting external influences such as public sentiment and economic indicators like Gross Domestic Product (GDP). To address these limitations, this study explores a hybrid approach that integrates Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models with sentiment analysis and GDP data to enhance Bitcoin price prediction accuracy. The study evaluates the predictive capabilities of these models under different scenarios. When trained on Bitcoin price data combined with sentiment analysis and GDP data, the ARIMA model achieved a Mean Absolute Error (MAE) of 2081.66, Root Mean Square Error (RMSE) of 2518.35, and an R-squared value of 0.9143. In comparison, when trained on Bitcoin data alone, it exhibited lower accuracy. The LSTM model demonstrated superior performance, achieving an MAE of 1253.24, RMSE of 1717.65, and an R-squared value of 0.9602 when incorporating sentiment and GDP data, significantly outperforming its standalone counterpart. The results highlight the effectiveness of integrating sentiment analysis and GDP data in cryptocurrency price prediction, demonstrating that hybrid models provide greater forecasting accuracy than traditional approaches. This study offers a robust framework for financial time series forecasting, aiding investors, analysts, and policymakers in making more informed decisions in the cryptocurrency market.
The advancement of e-health systems has resulted in substantial enhancements in healthcare delivery via effective data management and accessibility. The use of digital health solutions presents dangers to sensitive health information, including unauthorised access, privacy violations, and security weaknesses. This research presents a blockchain-based paradigm for privacy-preserving authentication and access control specifically designed for e-health systems. The architecture utilises the Ethereum blockchain, smart contracts, blind signatures, Proof of Authority (PoA) consensus, and one-way hash functions to improve data integrity, security, and privacy in a decentralised framework. The proposed methodology addresses computational efficiency and scalability issues via the implementation of lightweight cryptographic techniques, achieving an average authentication delay of 0.059 milliseconds, which represents a 4000-fold improvement compared to current approaches. The model exhibits a significant decrease in memory use, requiring just 0.0198 MB in contrast to the 96.98 MB required by benchmark models, and attains an average signature verification duration of 0.00092 milliseconds. The findings demonstrate the model’s capability for safe, efficient, and scalable applications in e-health, which guarantees privacy and adherence to regulatory norms.
We welcome the prospect of Guidelines on Article 102 TFEU and endorse the stated aims of the Draft Guidelines (ie enhancing legal certainty, helping undertakings to self-assess, and guiding the National Courts and National Competition Authorities). From an economics perspective, we also welcome the possible use of rebuttable presumptions for certain practices, but not for others (eg tying). Further, the approach of the Draft Guidelines to presumptions seems to imply a reversal of the burden of proof, which the EU Courts might arguably not accept. The Draft Guidelines move away from an economic, effects-based approach and do not fully acknowledge or embrace the modern legal approach of the EU Courts to Article 102 TFEU and lack clarity in many respects. Consequently, the Draft Guidelines offer limited guidance. The Draft Guidelines can be improved by: connecting the concept of ‘competition on the merits’ to harm to consumers; incorporating central concepts such as ‘theory of harm’; paying more than mere lip service to the ‘as efficient competitor’ principle; introducing safe harbours; and offering clarifications on the scope of certain presumptions and their rebuttal. The prohibition of an abuse of a dominant position found in Article 102 TFEU is a central tenet of EU competition law. In contrast to all other major aspects of EU competition law, such as Article 101 TFEU on anticompetitive multilateral conduct and merger control, Article 102 TFEU has so far operated with no Guidelines from the European Commission (EC). In 2009, the EC adopted a ‘Guidance Paper’ setting out the EC’s enforcement priorities in relation to exclusionary abuses, to provide ‘greater clarity and predictability’ regarding the ‘general framework of analysis’ employed by the EC in determining whether it should pursue a given case and to help undertakings to better assess whether their conduct may result in an intervention by the EC.1 The Guidance Paper was the culmination of a ‘reform’ of the EC’s approach to Article 102 TFEU and followed a period of extensive debate on, in particular, whether that approach should entail an economic, effects-based approach, in contrast to the EC’s and the European Courts’ historically formalistic approach.2 The Guidance Paper was received with different degrees of welcome in the competition community and in any case, its application in the EC’s decisional practice has not followed the enthusiasm with which the ‘reform’ was pursued.3 Against the background of the limited use of the Guidance Paper and its economic principles in the decisional practice by the EC, the Court of Justice of the European Union (CJEU) instilled an economic, effects-based approach to the application of Article 102 TFEU by its case law over the last decade. Thus, the ‘reform’ of the approach to Article 102 TFEU ultimately came not from the EC but from the CJEU, through seminal rulings such as Intel I, Unilever, SEN, and others, which arguably demonstrate a more economically informed, effects-based approach, moving away from the traditional, formalistic approach.4 This modern case law of the CJEU, often resulting in cases lost by the EC, clearly has implications for the development of the law on abuse of a dominant position. It is, thus, welcome that the EC has decided to adopt ‘Guidelines’ on exclusionary abuses given those significant judicial developments. The Draft Guidelines (DGs) published by the EC in August 2024 aim to enhance legal certainty, help firms to self-assess, and guide National Competition Authorities (NCAs) and National Courts.5 In contrast to the Guidance Paper, which was ‘not intended to constitute a statement of the law’,6 the Guidelines have the ambition to ‘codify the case law’7 and are not occupied with the setting of enforcement priorities. In addition to this ambition, importantly, the Guidelines arise out of a perception that the move towards an effects-based approach involves a ‘heightened substantive legal standard’ accorded to Article 102 TFEU, which ‘may inadvertently lead to undesirable outcomes’ such as false negatives by setting the bar for intervention too high.8 Thus, the EC declares its intention to adopt a ‘workable and effects-based approach’ to Article 102 TFEU, which has already been reflected in changes made to the Guidance Paper at the time of announcing the Guidelines and which will arguably be reflected in the forthcoming Guidelines.9 In this contribution, we examine, from a law and economics perspective, whether the Guidelines are likely to achieve their aims as currently drafted. We discuss the conformity of the DGs with the case law that the Guidelines purport to codify as well as with an economic, effects-based approach to Article 102 TFEU. This article contains two sections. In Section 2, after a brief summary of the DGs, we offer a critique of the DGs, focusing on the points where our views differ from the EC’s document, and identify areas for improvement. In Section 3, we offer some recommendations that the EC might want to the Section an the and of the DGs, the DGs provide principles and for and offer principles to conduct by a dominant is a of certain of conduct and to the DGs, a conduct by a dominant in an exclusionary abuse it a it from competition on the and it is of exclusionary The DGs identify different of The of for which the Courts have not a legal This others, and other than The is not may on which the Courts or the EC have not The to conduct which is as a to exclusionary This on the or all of its from the dominant in case of and of The of the as of conduct by a dominant that have no economic for that other than that of This paying for not on by a with the dominant or by We that it may also of The DGs on the that and to the which the of a legal the has an framework for the from competition on the the of the abuse as In relation to conduct which not competition on the in the the EC will such as whether the dominant or to other than competition law, of the of the service for the in of its over to a a conduct which an as efficient not such as the dominant position in The of the of abuse of whether the conduct at is of exclusionary the DGs which are likely to the legal burden of and the as and which different degrees of and on the of the the of the burden of the EC, which to that the practice is of exclusionary The which may be to the of a to exclusionary the position of the dominant the on the (eg of and the position of the of the the position of or of an exclusionary and to are for the of practices, exclusionary are to by the dominant of the of the in the at by their of such practices, a will be the EC that the practice from competition on the and has the to exclusionary be it through presumptions or an the dominant can its practice by an (ie an or that or the anticompetitive of the This as the of the of but with the burden of on the dominant be an not the of the but as a of the are through the of presumptions which not or an and of competition the and to exclusionary by the We the stated of the DGs and welcome the that by Guidelines the EC may be to enforcement of Article 102 TFEU cases through clarity of the as currently the DGs a of to the of this of the EC the to as dominant also firms with and the of safe for dominant firms which in Further, is on the EC to that for which is is that the of the abuse by the from competition on the on a concept which is to different and contains or no as as it is the framework is for that are to a legal This is that are to a legal are to of the abuse in the which the of the of abuse regarding are on in a different in the DGs The lack of clarity is by the that the of to exclusionary to all to a legal but This lack of clarity regarding the relation presumptions and the application of legal to certain of conduct the of the it is also the presumptions in the DGs can be in the of the case law of the as by the DGs regarding aspects of the of that the Guidelines may not enhance legal or help undertakings to the of their It is to that the Guidelines to the case law. the of on Article 102 TFEU is of the of the EC, that by the this legal as well as the that the case law on Article 102 TFEU is to the any legal the of Guidelines by the EC is their of to the of the law, than their of the law. The Guidelines provide by the EC may the law in certain aspects (eg for in its practice and the of the law (eg by the EC the law to by the as currently the DGs offer limited and do not legal In Section 3, we some that might help The of abuse is with some case law of the EU we the the concept of ‘competition on the merits’ is and to different by the Courts The DGs do not any to the concept a of that might be to that conduct from competition on the The DGs do provide an of ‘competition on the which it with The concept of competition on the conduct the scope of competition on the of the of economic and in to a in which from better and a of or improved and the DGs that a dominant can as an that conduct to competition on the in the case, the or exclusionary by the conduct are or by in of that the DGs do not to a to assess whether a conduct from competition on the in the We that the DGs and this of ‘competition on the merits’ by it that conduct from the is that has anticompetitive that is it or the for conduct be and the of the be given and to the that from competition on the to anticompetitive that or harm be in with the case law. a of the Courts it that Article 102 TFEU is with conduct to the of and that exclusionary should be as those ultimately or on or the case law the concept of ‘competition on the merits’ as a of the of the of exclusionary of Thus, the of ‘competition on the merits’ as a for which of exclusionary are anticompetitive has from the case law. 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case their of by the will be adopt a different approach to abuse the approach with a more and approach (eg of harm on This the DGs and the case law also the of the Guidelines to provide legal to In in relation to the which has been given a central in the DGs by of the Court of Justice has the of as efficient in where a competition that an has Article 102 TFEU and where that the the of those to efficient from the with that at the of the that those in the of the case, of as efficient as that from the The DGs any to as efficient in the of the of abuse that to be an of this of the Court of This that the DGs do not the case law, which to and this the that the of in the not the position of the Court that ‘competition on the merits’ by lead to the of efficient the Guidelines do not provide clarity on the of the the approach of the Guidelines can lead to a that of is It is that after the of the DGs, the has which or some of the by the EC in the that the the conduct at and its of the of an of 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B. N. Das, Babul Chandra Sarker, Amit Saha, Kanchon Kumar Bishnu · 9 authors
The exponential growth of cryptocurrency implementation in the USA has brought with it a surge in correlated risks, particularly in the form of scams that exploit the relative novelty and complexity of digital currencies. The primary objective of this study was to develop machine algorithms for identifying fraud trends in cryptocurrency transactions. By employing complex analysis, this research project attempted to identify certain trends and behaviors that fall under a variety of scams, providing a platform for effective detection and counter-strategies. This study will have a definite objective in terms of Bitcoin, Ethereum, and other high-profile cryptocurrencies in America when it comes to scam analysis. The scam-related transaction dataset comprised in-depth information regarding suspicious fraud activity in the cryptocurrency environment, such as a specific ID for a transaction, timestamps, values for transactions, and labels distinguishing between suspicious and legitimate activity. A variety of proven models were selected such as Logistic Regression, Random as well Multinomial Naive Bayes, where each model had its respective weaknesses and strengths. The Random Forest algorithm attained the highest accuracy, nearing perfection which underscores its robustness and reliability in classifying both legitimate and fraudulent reports. To effectively counter fraud in cryptocurrencies, U.S. policies must be strengthened with a merger of machine intelligence in them. Regulatory agencies have to work towards developing a system that encourages exchanges to utilize complex analysis for fraud detection, perhaps in terms of reduced compliance burden for entities with effective anti-fraud controls in position. Leveraging AI insights can go a long way in supporting investigations into scams in cryptocurrencies conducted by governments. By utilizing machine algorithms trained with datasets of past scams, governments can monitor and follow illicit fund flows through the blockchain with ease.
While the trend of decentralized governance is obvious (cryptocurrencies and blockchains are widely adopted by multiple sovereign countries), initiating governance proposals within Decentralized Autonomous Organizations (DAOs) is still challenging, i.e., it requires providing a low-level transaction payload, therefore posing significant barriers to broad community participation. To address these challenges, we propose a multi-agent system powered by Large Language Models with a novel Label-Centric Retrieval algorithm to automate the translation from natural language inputs into executable proposal transactions. The system incorporates DAOLang, a Domain-Specific Language to simplify the specification of various governance proposals. The key optimization achieved by DAOLang is a semantic-aware abstraction of user input that reliably secures proposal generation with a low level of token demand. A preliminary evaluation on real-world applications reflects the potential of DAOLang in terms of generating complicated types of proposals with existing foundation models, e.g. GPT-4o.
What is the brain that it can understand science?What is science that it can understand the brain?These two basic questions (with homage to Warren McCulloch in the framing) have guided my career, aiming to understand the brain and an effort to understand science. This journey has taken me from academic lab work to clinical research oversight and government policy to the emerging health & science technology industry and back to academia. It has now led me to co-lead, along with Dr. Jennifer Lovejoy of the Institute of Systems Biology, this section of Frontiers in Systems Biology -Systems Concepts, Theory and Policy in Biology and Medicine. Our journal Chief Editor, Dr. Yoram Vodovotz, has laid out the overarching vision for this and the other sections (Vodovotz, 2021). This Grand Challenge is an effort to add another layer of detail to the portions of that broad scope contained in our Systems Concepts section (Lovejoy, 2024).Systems biology and systems medicine have roots going back to at least World War II, when biologists and physiologists were recruited into the war effort in the United States and Britain, trained in computational approaches, and joined with engineers and mathematicians to solve complex problems with communications, radar, anti-aircraft guns and more (Churchill, 1949). This alignment led to the foundation of the field of cybernetics and the related Macy Conferences in the U.S. postwar, while in Britain, "This coalescing of biological, engineering, and mathematics frameworks would continue to great effect a few years later as the Ratio Club," (Husbands, 2008). In the decades that followed, this robust milieu of ideas would foster the development of everything from general systems theory and information theory to artificial intelligence (AI) and cognitive science (Pickering, 2010). Despite this early alignment, it would be decades before systems biology and systems medicine arose as formal fields of inquiry (Green, 2017).Science has arguably been the most effective way of generating and validating new knowledge for the past few centuries. New technologies and computing approaches now provide us with novel tools to accelerate this process. While early work is being done to explore the use of these new tools for science, these have been limited in success to real-world application to detailed aspects of biology and medicine (McCoy, 2024). A comprehensive conceptual framework may be a more effective way to realize the value of technology in accelerating science. Modern science is not a simple holistic process, but an amalgam of processes and interests that have accumulated over centuries.By analyzing this system of science, we can better synthesize a new approach to using the array of emerging technologies now available. This will require us to revisit the current human and institutional processes that govern the creation of new scientific knowledge. A human and machine hybrid approach, aligned with a governance in the classic cybernetic style (i.e. control and communication in humans and machines), may allow us to optimize our scientific efforts and advance knowledge for the betterment of all of humanity.There has been much excitement about the potential of emerging technologies applied to science in recent yearsfrom AI to applications of blockchain technologies and web3 applied as decentralized science (DeSci) (Weidener, 2024). In these nascent efforts there has often been an oversimplification of science in order to capture technical requirements to automate or simulate biomedical research.Science is not done by a single person or organization. Science embodies the contribution of multiple individualswhose brains are themselves collections of dozens of subsystems (Kirby, 2024)processed through a series of refinement and testing. The results of these are moved through a longitudinal process of validation, contextual framing against prior accumulated knowledge, and consensus determination of evidence level and confidence in the results. Only then does this new knowledge contribute to the body of generalized knowledge we applied to the real world.Creating a new technology-accelerated knowledge system for biomedical science -what I'm calling here Scientia Machinamay be best approached through first articulating the conceptual and epistemological framework of the current system of biomedical science as it moves from data to information to evidence to knowledge and its application. Along the way it passes through layers of trust and is eventually captured in the artifacts of biomedical science we have come to rely on and expect. For applications of emerging technologysuch as the automated complex information processing of AI and the automated trust and governance of blockchainto be most beneficial to science, we should use them to systematically augment and accelerate these processes and creation of the artifacts of science while maintaining or improving the basic conceptual framework of biomedical knowledge discovery and implementation. Eventually parts of the current system may be sundowned leading to an even greater acceleration of science.This Scientia Machina framework starts with identifying key layers of trust in the biomedical bench to bedside process of evidence based medicine. Here I have proposed five layers of trust along with examples of their current artifacts and processes, plus potential approaches to augmenting these with technology and related adjustment to the current workflow (Figure 1).Data Layer -Data are collected in experimental and/or clinical context, often based on specific methodology. The principal investigator (PI) and team, along with the equipment and techniques used, are trusted to produce and capture explainable and reproducible data. This layer is only sometimes made transparent and rarely validated.Future of Data -Data are verifiable through trackable provenance and alignment with related metadata (e.g. demographics, treatment delivery details, device and equipment specifications, etc.). Data can be accessed for querying and algorithm training without moving, copying or exposing the data.Information Layer -Data are combined in datasets with contextual meta-data (e.g. demographics of research participants). The PI and team are trusted to compile, store and manage this data. It is increasingly becoming requested by funders and publishers to be made available. Some programs promote dataset sharing through centralized repositories or direct PI to PI contact.Future of Information -Data confidence fabrics allow sorting combined datasets based on confidence levels for each data point related to their associated metadata, with deployable programming to temporarily convert non-standard data into a calculable or trainable standard.Evidence Layer -Analysis of the datasets and testing hypotheses produces results that interpreted as findings. These are presented as novel assertion, backed by the data and methods, and put into the context of previously identified findings in the field in the form of a manuscript submitted for peerreview. The journal editors and peer-reviewers are trusted to confirm the assertions are supported by the evidence, fit (or convincingly contradict) previously established knowledge in the field.Future of Evidence -Swarm approach, i.e. networked, auditable crowd-sourcing, to peer review with a wider array of contributors with inputs weighted based on preset governance and continuous crowd feedback for nearer to real-time review with broader, multi-discipline input.Knowledge Layer -Combined sets of published articles are reviewed by a group of experts against certain criteria to answer specific questions about the state of evidence in the field as systematic reviews and meta-analyses to provide the most up-to-date knowledge in the specific area of focus.The groups of authors along with editors and peer-reviewers of those systematic reviews and metaanalyses are trusted to have executed and validated, respectively, a thorough and sound assessment of the evidence for the area in question to provide new knowledge.Future of Knowledge -Swarm approach (see above) to systematic review with network on demand request for new or updated reviews of existing evidence along with evidence threshold signals (i.e. sufficient new evidence in a particular areas prompts new or updated systematic review).Applied Knowledge Layer -Applications of knowledge can come in various forms, including pharmaceuticals, devices and procedures. The application of knowledge is periodically assessed for incorporation into clinical practice guidelines (CPG) and similar clinical guidance documents. The CPG group is trusted to have found and appropriately graded all of the available evidence and refined knowledge on a topic area to best inform clinicians how to address the area optimally.Future of Knowledge Application -Networked clinical practice guideline wiki (collaboratively edited living document) allowing for continuous, network refereed input and update of new knowledge.Each of these layers and their future states can be augmented, enhanced, accelerated and potentially replaced with appropriate applications of an array of automated processing and trust technologies. Additional administrative areas of biomedical research such as gap analysis, funding, regulatory review and more can be similarly improved.The call to action for this Grand Challenge is to: a) Consider the core elements of what we need to maintain and continue to elevate from our past and current successful biomedical research and knowledge translation effort, along with areas where those efforts have been flawed, corrupt or unsuccessful. b) Critique (and adjust or replace as needed) the Scientia Machina framework proposed here as the backbone for the layers of trust that are the core elements to be maintained as we continue to bring new technologies into biomedical research to accelerate and improve science. c) Capture and assess those current pilots to apply emerging technology -especially within AI (complex information processing) and DeSci (automated governance, auditing and/or incentivization) as umbrella categories for these effortsand place them in the context of a broader framework of what we are trying to achieve with biomedical research. d) Conceptualize gaps in our current efforts along with bridges from the current status quo to the desired future that may give us a better chance of success at transformational change to the systems of biomedical research and knowledge translation. e) Communicate all aspects of the above areas in appropriate venues of biology, medicine, technology and policy. This includes formal submissions of manuscript on any related topics to this journal section and its partnered sections as appropriate.This proposed conceptual framework is merely a jumping off point for broader consideration of how to maintain the core elements of the trust we have imbued in biomedical research as we continue to explore applications of emerging technology to improve its quality, manage its costs, and accelerate its contribution to the health and well-being of everyone. In the not so distant future, it is conceivable that we may be able to make all available relevant data on a topic or a patient accessible to any researcher to make AI-augmented and blockchain-audited hypothesis testing to provide near realtime, peer-validated contributions to evidence-based medicine. This could allow clinicians to query and access this near real-time evidence as part of compressing the 17 years it takes to go from bench to bedside by a factor of 10,000xgiving us new, actionable evidence-based precision medicine for patients in under a day. This future is within reach. Aligning behind a shared framework like Scientia Machina can bring it into our reality even faster. Better science. Cheaper research. Faster Miracles.STM is the sole author, having conceived, written, and edited this manuscript.
In recent years, the Ethereum platform has witnessed a proliferation of smart contracts, accompanied by exponential growth in total value locked (TVL). High-TVL smart contracts often require complex numerical computations, particularly in mathematical financial models used by many decentralized applications (DApps). Improper calculations can introduce numerical defects, posing potential security risks. Existing research primarily focuses on traditional numerical defects like integer overflow, and there is currently a lack of systematic research and effective detection methods targeting new types of numerical defects. In this paper, we identify five new types of numerical defects through the analysis of 1,199 audit reports by utilizing the open card method. Each defect is defined and illustrated with a code example to highlight its features and potential consequences. We also propose NumScout, a symbolic execution-based tool designed to detect these five defects. Specifically, the tool combines information from source code and bytecode, analyzing key operations such as comparisons and transfers, to effectively locate defects and report them based on predefined detection patterns. Furthermore, NumScout uses a large language model (LLM) to prune functions which are unrelated to numerical operations. This step allows symbolic execution to quickly enter the target function and improve runtime speed by 28.4%. We run NumScout on 6,617 real-world contracts and evaluated its performance based on manually labeled results. We find that 1,774 contracts contained at least one of the five defects, and the tool achieved an overall precision of 89.7%.
Smart contracts on blockchain networks autonomously execute applications based on predefined conditions, making their security-critical due to the potential for significant financial losses from vulnerabilities. Current vulnerability detection algorithms commonly rely on expert-defined rules, which are prone to errors and insufficient for identifying complex vulnerability patterns . Given the immutability of smart contracts post-deployment, ensuring security before deployment is essential. This research presents Block-wise Abstract Syntax Tree based Federated Graph Neural Networks (BAST-FeGNN), a novel approach combining block-wise abstract syntax tree and Federated Graph Neural Networks (FeGNN) to detect code clones and multiclass vulnerabilities in Ethereum smart contracts. The BAST-FeGNN method operates in three stages: it first extracts security-related patterns from the base code using an abstract syntax tree; then, it constructs and normalizes a contract graph using FeGNN to capture critical nodes, analyze data and control flows. This integration of graph-based feature extraction with pattern matching allows precise detection of vulnerabilities like access control issues, reentrancy, and unchecked calls, as well as identifying code clones. Finally, the method pools these features for comprehensive vulnerability detection . BAST-FeGNN significantly enhances vulnerability detection accuracy and scalability, outperforming existing models with an accuracy of 95.35%, recall of 95.58%, F1-score of 95.80%, and precision of 96.10%, making it a robust solution for securing blockchain applications.
Decentralized Autonomous Organizations (DAOs) are a type of Decentralized Applications (DApps) that utilize smart contracts to support governance processes. To achieve a high degree of utility of the system, stakeholders need to identify a suitable organizational structure in the early stages of design. While Model-Driven Development (MDD) methods are established for DApp and smart contract design, they lack specialization for modeling the organizational structures of DAOs. To address this gap, we propose a modeling language and a method which support crucial DAO design and development phases. The method is evaluated through an in vivo case study. Unlike existing solutions, comprehensive stepwise guidance is provided by our method for both technical and non-technical stakeholders involved in DAO development from the initial stages of the project.
Jorge Álvaro González, Ana María Saiz García, Victor Monzon Baeza
In a globalized and interconnected world, interoperability has become a key concept for advancing tactical scenarios. Federated Coalition Networks (FCN) enable cooperation between entities from multiple nations while allowing each to maintain control over their systems. However, this interoperability necessitates the sharing of increasing amounts of information between different tactical assets, raising the need for higher security measures. Emerging technologies like blockchain drive a revolution in secure communications, paving the way for new tactical scenarios. In this work, we propose a blockchain-based framework to enhance the resilience and security of the management of these networks. We offer a guide to FCN design to help a broad audience understand the military networks in international missions by a use case and key functions applied to a proposed architecture. We evaluate its effectiveness and performance in information encryption to validate this framework.
Mostafa Chegenizadeh, Sina Rafati Niya, Claudio J. Tessone
Blockchain technology has recently gained widespread popularity as a practical method of storing immutable data while preserving the privacy of users by anonymizing their real identities. This anonymization approach, however, significantly complicates the analysis of blockchain data. To address this problem, heuristic-based clustering algorithms as an effective way of linking all addresses controlled by the same entity have been presented in the literature. In this paper, considering the particular features of the Extended Unspent Transaction Outputs accounting model introduced by the Cardano blockchain, two new clustering heuristics are proposed for clustering the Cardano payment addresses. Applying these heuristics and employing the UnionFind algorithm, we efficiently cluster all the addresses that have appeared on the Cardano blockchain from September 2017 to January 2023, where each cluster represents a distinct entity. The results show that each medium-sized entity in the Cardano network owns and controls 9.67 payment addresses on average. The results also confirm that a power law distribution is fitted to the distribution of entity sizes recognized using our proposed heuristics.
The integration of blockchain technology with data analytics is essential for extracting insights in the cryptocurrency space. Although academic literature on blockchain data analytics is limited, various industry solutions have emerged to address these needs. This paper provides a comprehensive literature review, drawing from both academic research and industry applications. We classify blockchain analytics tools into categories such as block explorers, on-chain data providers, research platforms, and crypto market data providers. Additionally, we discuss the challenges associated with blockchain data analytics, including data accessibility, scalability, accuracy, and interoperability. Our findings emphasize the importance of bridging academic research and industry innovations to advance blockchain data analytics.
Threshold Signature Scheme (TSS) protocols have gained significant attention over the past ten years due to their widespread adoption in cryptocurrencies. The adoption is mainly boosted by Gennaro and Goldfedder's TSS protocol. Since then, various TSS protocols have been introduced with different features, such as security and performance, etc. Large organizations are using TSS protocols to protect many digital assets, such as cryptocurrency. However, the adoption of these TSS protocols requires an understanding of state-of-the-art research in threshold signing. This study describes the holistic view of TSS protocols, evaluates cutting-edge TSS protocols, highlights their characteristics, and compares them in terms of security and performance. The evaluation of these TSS protocols will help the researchers address real-world problems by considering the relevant merits of different TSS protocols.