Since the introduction of Bitcoin, the first cryptocurrency, virtual currencies have become a topic of increasing public concern. Bitcoin trading is highly speculative and involves significant risks, as its value can fluctuate dramatically over time, with no single party held accountable for these changes. This study focuses on the protection of investors engaged in Bitcoin transactions on exchange platforms in Indonesia under positive law. The research employs a normative juridical approach, with both primary and secondary data sourced from legal texts, regulations, and relevant literature. The findings indicate that Bitcoin transactions in Indonesia primarily involve the sale of commodity assets through exchange platforms, which function as physical traders of crypto assets. Regulatory frameworks established by futures regulatory bodies play a crucial role in preventing fraud and safeguarding legal rights. According to Indonesian Contract Law, as outlined in the Civil Code (Burgerlijk Wetboek, BW), Bitcoin transactions are considered “legal” when they fulfill the contractual conditions specified in Article 1320. Consequently, investors are legally protected from both criminal and civil liabilities due to the validity of these transactions.
The paper demonstrates the nonsense of using Bitcoin in financial investments. By using mean-variance financial analysis, stochastic dominance, CVaR, and the Shapley value theory as analytical statistical models, I show how Bitcoin performs poorly by comparing it against other traded assets. The conclusion is reached by analyzing daily freely available market data for the period 2018–2023.
While traditional behavioral finance theories such as the TRA, TPB, and TAM have provided substantial insights, their application to the rapidly evolving digital finance sector, particularly cryptocurrency markets , has been limited. Addressing this gap, our study integrates Digital Trust Theory (DTT) with these frameworks to examine the role of government support in Crypto Adoption (CA) within Vietnam's dynamic but unregulated market, a prominent emerging market in global crypto trading. Utilizing Structural Equation Modeling , we processed data collected from 255 participants using SmartPLS 4.0 to explore complex relationships among User Characteristics (UC), Technology Characteristics (TC), External Environment (EX), and their impacts on Crypto Trust (CT) and Crypto Adoption CA. This dataset, comprising responses from a diverse array of participants including tech-savvy youths, business professionals, and financial experts across various regions of Vietnam, provides a robust basis for understanding the nuanced influences on cryptocurrency behaviors. Our findings underscore the significant mediating roles of Crypto Trust and governmental regulation, highlighting the crucial influence of External Environment factors on trust dynamics. These insights not only contribute to academic discourse by refining traditional behavioral finance theories for the digital era but also offer practical guidance for fostering a sustainable cryptocurrency market, thereby supporting economic development and financial security in Vietnam.
Endrizal Ridwan, Mailinda Tri Wahyuni, Dwi Fitrizal Salim
This study aims to analyze the impact of news related to Central Bank Digital Currency (CBDC) on the stock and cryptocurrency markets in the United States. Using the Time-Varying Parameter Vector Autoregression (TVP-VAR) method, this study examines the responses of the S&P 500 index, the CBOE Volatility Index (VIX), Bitcoin trading volume, and Ethereum trading volume to CBDC news from January 2020 to December 2023. The results indicate that CBDC news has a positive effect on stock market prices, but its impact on market volatility is negligible. Furthermore, Bitcoin and Ethereum trading volumes exhibit a declining trend in response to the rapid development of CBDC news. Although CBDC development is still in its early stages, these findings provide insights into the potential influence of CBDC news on financial market behavior, particularly in shaping investor sentiment and digital asset trading patterns. The study suggests that policymakers and investors should closely monitor CBDC developments, as they may gradually affect the financial ecosystem. Future research should further explore the long-term effects of CBDCs on financial market stability and cryptocurrency adoption.
Ahmad Jurnaidi Wahidin, Rayhan Maulana Sugiharto Putra
Penelitian ini mengkaji potensi investasi dalam cryptocurrency dengan menerapkan metode Net Present Value (NPV) sebagai sistem pendukung keputusan untuk menilai dan membandingkan tiga cryptocurrency yaitu Bitcoin (BTC), Ethereum (ETH), dan Binance Coin (BNB). Cryptocurrency telah muncul sebagai instrumen investasi yang menarik perhatian besar dalam beberapa tahun terakhir, terutama di kalangan milenial, karena dianggap sebagai mata uang masa depan. Dalam penelitian ini, kinerja ketiga cryptocurrency dianalisis berdasarkan tiga kriteria utama: Tingkat Pertumbuhan Tahunan (Annual Growth Rate), Volume Perdagangan (Trading Volume), dan Ketersediaan di Platform Pertukaran Terkemuka (Availability on Major Exchange Platforms). Data dikumpulkan dari berbagai sumber tepercaya seperti bursa cryptocurrency dan laporan keuangan. Perhitungan NPV dilakukan untuk mengukur nilai sekarang dari arus kas masa depan yang diharapkan dari masing-masing cryptocurrency. Hasil penelitian menunjukkan bahwa Binance Coin (BNB) memiliki nilai NPV tertinggi sebesar $29.416,91, diikuti oleh Ethereum (ETH) dengan NPV sebesar $26.085,74, dan Bitcoin (BTC) dengan NPV sebesar $22.948,93. Ini menunjukkan bahwa BNB menawarkan nilai investasi terbaik di antara ketiga cryptocurrency yang dianalisis, berdasarkan kriteria yang ditetapkan. Penelitian ini menjadi sistem pendukung bagi investor untuk membuat keputusan investasi yang lebih informasional dan beralasan dalam pasar cryptocurrency yang fluktuatif.
Phishing is a serious threat to cryptocurrency networks; Bitcoin and Ethereum are prime targets for these attacks. This paper discusses some aspects of phishing attacks on these platforms. While the simpler architecture of Bitcoin leads to more direct phishing attempts, the more complex ecosystem in Ethereum introduces a wide range of attack vectors through dApps and smart contracts. A comparative analysis of phishing attacks in both blockchains shows that while both have their fair share of attacks, Bitcoin seems to bear the brunt of phishing attacks. Current defense strategies, like 2FA and anti-phishing tools, as well as recommendations for increasing network security against phishing are discussed in this paper. Understanding these phishing mechanisms is crucial in strengthening the security of blockchain platforms and mitigating future attacks.
Scaling blockchain performance through parallel smart contract execution has gained significant attention, as traditional methods remain constrained by the performance of a single virtual machine (VM), even in multi-chain or Layer-2 systems. Parallel VMs offer a compelling solution by enabling concurrent transaction execution within a single smart contract, using multiple CPU cores. However, Ethereum's sequential, shared-everything model limits the efficiency of existing parallel mechanisms, resulting in frequent rollbacks with optimistic methods and high overhead with pessimistic methods due to state dependency analysis and locking.
ABSTRACT This study examines the connection between Bitcoin and global factors, including the VIX, the oil price, the US dollar index, the gold price, and interest rates estimated using the Federal funds rate and treasury securities rate, for forecasting analysis. Deep learning methodologies, including LSTM, GRU, CNN, and TFT, with machine learning algorithms such as XGBoost, LightGBM, and SVR, were employed to identify the optimal prediction model for the Bitcoin price. The findings indicate that the TFT model is the most successful predictive approach, with the gold price identified as the most relevant component in determining the Bitcoin price. After the gold indicator, the US dollar index was a substantial factor in the explanation of the Bitcoin price. The TFT model also included regulatory decisions and global events. It was estimated that the Bitcoin price was significantly influenced by the COVID‐19 pandemic. After that, global climate events and China mining ban strongly affected the Bitcoin price. These findings indicate that regulatory decisions and global events determine the Bitcoin price in addition to macroeconomic factors. The VAR analysis was employed as a robustness check. The results indicate that gold and oil prices have a strong negative influence on Bitcoin, particularly in the long term. The paper has significant policy implications for investors, portfolio managers, and scholars.
The emerging markets are fast gaining relevance in the revolution of digital finance. With the continued growth of cryptocurrency and decentralized finance (DeFi) technologies, governments in these jurisdictions are confronted by a reality crisis, namely, how they can implement tax regimes that are both revenue-generating and innovation-friendly without toxicizing the regulatory landscape. In the paper, the complex issues of taxation of crypto assets and DeFi activity in emerging economies are discussed, structural, technological, and institutional barriers to the conventional tax framework are presented and complicate the taxation of cryptocurrency and related activities. A mixed methods strategy (applying qualitative stakeholder information to quantitative modelling and comparative policy research) helps to reveal how current tax regimes, in most cases, fall behind market development, which results in loss of revenue, enforcement gaps as well as non-intended incentives to informal economic responses. We suggest that effective policy frameworks should strike the right balance between revenue collection and fairness, enforceability and respect of decentralized spirit of DeFi. We identify practical solutions, including adaptive regulatory sandboxes, blockchain-based reporting solutions, and collaborative international standards that can all help build a resilient but adaptable tax regime by reviewing country case studies and the best practices of other countries. The results of our findings indicate that the emerging markets can use technology and cross sector partnership to make their tax systems engines of compliance and innovation. Finally, the study offers a roadmap to policymakers in an attempt to have fair, efficient, and progressive cryptocurrency taxation of crypto assets and decentralized finance.
Decentralized financing may become an effective tool for developing financial markets in the Russian Federation in the foreseeable future, but its legitimization is associated with a number of threats and occurs in conditions of uncertainty. At the same time, postponing decisions in the development of the state's financial policy may lead to even greater costs associated with the departure of a promising segment of the new financial market into the shadows. The paper considers the opportunities and threats of decentralized financing development for traditional financial institutions in the Russian Federation. The study was prepared using the methods of conceptual, comparative analysis, financial and economic foresight. Results: the concept of decentralized financing is clarified, the differences between decentralized financing and digital financial assets previously legalized in Russia are considered. International experience of companies - traditional financial intermediaries - entering decentralized financing is presented, which is mainly in the nature of a test approbation of possibilities. Based on international statistics, an increase in the share of traditional financial companies in the volume of decentralized borrowings is shown. The article describes the diversity of development opportunities in the decentralized finance market for such classic financial intermediaries as commercial banks. Solutions for the development of financial policy in the Russian Federation are proposed, the need for legalization and the fastest possible construction of a sovereign infrastructure of decentralized finance with the safe involvement of traditional financial institutions is argued.
Open access
Digitalization and Economic Development in Agriculture
Economic, Social, and Public Health Issues in Russia and Globally
Penelitian ini bertujuan untuk mengkaji peran gamifikasi dalam mendorong komitmen Corporate Social Responsibility (CSR) digital pada startup sosial yang berbasis teknologi Web3. Dengan meningkatnya adopsi Web3 dan model bisnis terdesentralisasi, pendekatan baru diperlukan untuk membangun keterlibatan pengguna dan memperkuat nilai sosial perusahaan. Penelitian ini menggunakan metode campuran (mixed methods) dengan pendekatan studi kasus pada tiga startup sosial berbasis Web3 yang telah mengimplementasikan elemen gamifikasi dalam program CSR digital mereka. Data dikumpulkan melalui wawancara mendalam, observasi partisipatif, serta analisis data transaksi berbasis blockchain. Hasil penelitian menunjukkan bahwa elemen gamifikasi seperti reward token, leaderboard berbasis reputasi, dan tantangan komunitas dapat meningkatkan partisipasi pengguna dalam aktivitas sosial dan memperkuat persepsi terhadap komitmen sosial startup. Temuan penting menunjukkan bahwa keterlibatan pengguna meningkat hingga 45% setelah penerapan strategi gamifikasi yang terdesain secara strategis dan berbasis insentif digital. Simpulan dari penelitian ini adalah bahwa gamifikasi berperan signifikan dalam menginternalisasi nilai CSR digital pada platform Web3, serta memperkuat loyalitas pengguna terhadap misi sosial startup. Rekomendasi diberikan agar startup sosial merancang gamifikasi berbasis transparansi dan insentif berkelanjutan untuk memaksimalkan dampak sosial di era ekonomi digital
Gabriel Fernández-Blanco, Pedro García-Cereijo, David Lema-Núñez, Diego Ramil-López · 8 authors
In the last years, especially since the COVID-19 pandemic, precision medicine platforms emerged as useful tools for supporting new tests like the ones that detect the presence of antibodies and antigens with better sensitivity and specificity than traditional methods. In addition, the pandemic has also influenced the way people interact (decentralization), behave (digital world) and purchase health services (online). Moreover, there is a growing concern in the way health data are managed, especially in terms of privacy. To tackle such issues, this article presents a sustainable direct-to-consumer health-service open-source platform called HELENE that is supported by blockchain and by a novel decentralized oracle that protects patient data privacy. Specifically, HELENE enables health test providers to compete through auctions, allowing patients to bid for their services and to keep the control over their health test results. Moreover, data exchanges among the involved stakeholders can be performed in a trustworthy, transparent and standardized way to ease software integration and to avoid incompatibilities. After providing a thorough description of the platform, the proposed health platform is assessed in terms of smart contract performance. In addition, the response time of the developed oracle is evaluated and NIST SP 800-22 tests are executed to demonstrate the adequacy of the devised random number generator. Thus, this article shows the capabilities and novel propositions of HELENE for delivering health services providing an open-source platform for future researchers, who can enhance it and adapt it to their needs.
Maryam Bahrani, Michael Neuder, S. Matthew Weinberg
Selfish miners selectively withhold blocks to earn disproportionately high revenue. The vast majority of the selfish mining literature focuses exclusively on block rewards. Carlsten et al. [2016] is a notable exception, observing that similar strategic behavior is profitable in a zero-block-reward regime (the endgame for Bitcoin's quadrennial halving schedule) if miners are compensated with transaction fees alone. Neither model fully captures miner incentives today. The block reward remains 3.125 BTC, yet some blocks yield significantly higher revenue. For example, congestion during the launch of the Babylon protocol in August 2024 caused transaction fees to spike to 9.52 BTC. Our results are both practical and theoretical. Of practical interest, we study selfish mining profitability under a combined reward function that more accurately models miner incentives. This analysis enables us to make quantitative claims about protocol risk (e.g., the mining power at which a selfish strategy becomes profitable is reduced by 22% when optimizing over the combined reward function versus block rewards alone) and qualitative observations (e.g., a miner considering both block rewards and transaction fees will mine more or less aggressively respectively). These practical results follow from our novel model and methodology, which constitute our theoretical contributions. We model general, time-accruing stochastic rewards, which requires explicit treatment of difficult adjustment and randomness; we characterize reward function structure through a set of properties (e.g., that rewards accrue only as a function of time). We present a new methodology to analytically calculate expected selfish miner rewards under a broad class of stochastic reward functions and validate our method numerically by comparing it with the existing literature and simulating the combined reward sources directly.
Ben Berger, Edward W. Felten, Akaki Mamageishvili, Benny Sudakov
Optimistic rollups rely on fraud proofs -- interactive protocols executed on Ethereum to resolve conflicting claims about the rollup's state -- to scale Ethereum securely. To mitigate against potential censorship of protocol moves, fraud proofs grant participants a significant time window, known as the challenge period, to ensure their moves are processed on chain. Major optimistic rollups today set this period at roughly one week, mainly to guard against strong censorship that undermines Ethereum's own crypto-economic security. However, other forms of censorship are possible, and their implication on optimistic rollup security is not well understood. This paper considers economic censorship attacks, where an attacker censors the defender's transactions by bribing block proposers. At each step, the attacker can either censor the defender -- depleting the defender's time allowance at the cost of the bribe -- or allow the current transaction through while conserving funds for future censorship. We analyze three game theoretic models of these dynamics and determine the challenge period length required to ensure the defender's success, as a function of the number of required protocol moves and the players' available budgets.
A payment channel network is a blockchain-based overlay mechanism that allows parties to transact more efficiently than directly using the blockchain. These networks are composed of payment channels that carry transactions between pairs of users. Due to its design, a payment channel cannot sustain a net flow of money in either direction indefinitely. Therefore, a payment channel network cannot serve transaction requests arbitrarily over a long period of time. We introduce DEBT control, a joint routing and flow-control protocol that guides a payment channel network towards an optimal operating state for any steady-state demand. In this protocol, each channel sets a price for routing transactions through it. Transacting users make flow-control and routing decisions by responding to these prices. A channel updates its price based on the net flow of money through it. The protocol is developed by formulating a network utility maximization problem and solving its dual through gradient descent. We provide convergence guarantees for the protocol and also illustrate its behavior through simulations.
Throughout the history from pre-industry 4.0 to post-industry 4.0, cybersecurity at banks has undergone significant changes. Pre-industry 4.0 cyber security at banks relied on individual security methods that were highly manual and had low accuracy. When moving to post-industry 4.0, cybersecurity at banks had a major turning point with security methods that combined different technologies such as Artificial Intelligence (AI), Blockchain, IoT, automating necessary processes and significantly increasing the defence layer for banks. However, along with the development of new technologies, the current challenge of cybersecurity at banks lies in scalability, high costs and resources in both money and time for R&D of defence methods along with the threat of high-tech cybercriminals growing and expanding. This report goes from introducing the importance of cybersecurity at banks, analyzing their management, operational and business objectives, evaluating pre-industry 4.0 technologies used for cybersecurity at banks to assessing post-industry 4.0 technologies focusing on Artificial Intelligence and Blockchain, discussing current policies and practices and ending with discussing key advantages and challenges for 4.0 technologies and recommendations for further developing cybersecurity at banks.
The ``EIP-1599 algorithm'' is used by the Ethereum blockchain to assemble transactions into blocks. While prior work has studied it under the assumption that bidders are ``impatient'', we analyze it under the assumption that bidders are ``patient'', which better corresponds to the fact that unscheduled transactions remain in the mempool and can be scheduled at a later time. We show that with ``patient'' bidders, this algorithm produces schedules of near-optimal welfare, provided it is given a mild resource augmentation (that does not increase with the time horizon). We prove some generalizations of the basic theorem, establish lower bounds that rule out several candidate improvements and extensions, and propose several questions for future work.
Decentralized exchanges (DEXs) face persistent challenges in liquidity retention and user engagement due to inefficiencies in conventional automated market maker (AMM) designs. This work proposes a dual-mechanism framework to address these limitations: a ``Better Market Maker (BMM)'', which is a liquidity-optimized AMM based on a power-law invariant ($X^nY = K$, $n = 4$), and a dynamic rebate system (DRS) for redistributing transaction fees. The segment-specific BMM reduces impermanent loss by 36\% compared to traditional constant-product ($XY = K$) models, while retaining 3.98x more liquidity during price volatility. The DRS allocates fees ($γV$, $γ\in \{0.003, 0.005, 0.01\}$) with a rebate ratio $ρ\in [0.3, 0.4]$ to incentivize trader participation and maintain continuous capital injection. Simulations under high-volatility conditions demonstrate impermanent loss reductions of 36.0\% and 40\% higher user engagement compared to static fee models. By segmenting markets into high-, mid-, and low-volatility regimes, the framework achieves liquidity depth comparable to centralized exchanges (CEXs) while maintaining decentralized governance and retaining value within the cryptocurrency ecosystem.
Active Learning (AL) is a machine learning technique where the model selectively queries the most informative data points for labeling by human experts. Integrating AL with crowdsourcing leverages crowd diversity to enhance data labeling but introduces challenges in consensus and privacy. This poster presents CrowdAL, a blockchain-empowered crowd AL system designed to address these challenges. CrowdAL integrates blockchain for transparency and a tamper-proof incentive mechanism, using smart contracts to evaluate crowd workers' performance and aggregate labeling results, and employs zero-knowledge proofs to protect worker privacy.
Decentralized lending protocols within the decentralized finance ecosystem enable the lending and borrowing of crypto-assets without relying on traditional intermediaries. Interest rates in these protocols are set algorithmically and fluctuate according to the supply and demand for liquidity. In this study, we propose an agent-based model tailored to a decentralized lending protocol and determine the optimal interest rate model. When the responses of the agents are linear with respect to the interest rate, the optimal solution is derived from a system of Riccati-type ODEs. For nonlinear behaviors, we propose a Monte-Carlo estimator, coupled with deep learning techniques, to approximate the optimal solution. Finally, after calibrating the model using block-by-block data, we conduct a risk-adjusted profit and loss analysis of the liquidity pool under industry-standard interest rate models and benchmark them against the optimal interest rate model.
The integration of Artificial Intelligence (AI) and Blockchain represents a paradigm shift in digital transformation, offering enhanced security, scalability, and automation. While previous research has explored these technologies independently, this study provides a comprehensive review of their convergence, synthesizing insights across multiple domains such as finance, healthcare, and supply chain management. The findings highlight the bidirectional enhancement of AI-Blockchain integration: Blockchain reinforces AI’s reliability by ensuring data immutability and transparency, whereas AI optimizes Blockchain efficiency through intelligent consensus mechanisms and fraud detection. However, significant challenges remain, including scalability constraints, computational overhead, and regulatory concerns. This study contributes to the theoretical understanding of AI-Blockchain synergy by integrating concepts from Computational Trust Theory and Decentralized Ledger Theory. Practically, it provides actionable insights for industry stakeholders, particularly in decentralized finance, privacy-preserving AI models, and secure digital transactions. The novelty of this research lies in its examination of AI-Blockchain integration through geographical and temporal trends, revealing disparities in adoption and regulatory responses. Despite its potential, real-world implementation remains limited, necessitating further empirical validation and exploration of emerging technologies such as quantum computing and the Internet of Things (IoT). By addressing these gaps, this study serves as a foundation for future research and policy development, advocating for interdisciplinary collaboration to ensure secure, efficient, and ethical AI-Blockchain ecosystems. The implications extend beyond academia, offering strategic guidance for practitioners and policymakers navigating the complexities of this technological convergence.
The advent of decentralised applications across a range of sectors has led to a growing emphasis on the research and development of methods to identify vulnerabilities in smart contracts for decentralised applications. However, current detection techniques have been found to have limitations in terms of accuracy and the number of false alarms they generate. In order to address the aforementioned issues, this paper puts forth a modular vulnerability detection model, designated as BAMC. The method initially utilises the word2vec model to derive the word vector representation of the smart contract, subsequently extracting the word order information through a bidirectional long short-term memory network. Subsequently, the attention mechanism and max-pooling operation are employed to process the word order information, thereby obtaining fine-grained features and key features. Ultimately, explicit bounded-degree feature interactions are achieved through the combination of deep and cross networks, thus enabling the detection of reentrancy vulnerabilities and timestamp vulnerabilities. The experimental results demonstrate that the proposed method exhibits superior performance in comparison to existing techniques, with significantly higher values for various indexes. Notably, the reentrancy vulnerability and the - of timestamp vulnerability reach 86.14 and 91.43 , respectively.