In modern conditions, digitalization represents a complex phenomenon that actively encompasses various spheres of social life. This article examines the implementation of smart contracts, which are changing established approaches to transport services in the context of digital transformation. It is concluded that, despite the presence of the term âcontractâ in the name âsmart contractâ, it remains, by its legal nature, a computer program.
<div class="section abstract"><div class="htmlview paragraph">As electric vehicles adoption becomes more common, power grid operators are facing new challenges in managing the unpredictable and varying energy demands in the existing electrical infrastructure. Moreover, the cost of Electric vehicle is high when compared to fuel vehicle it has limited access to charging infrastructure along with the driving range that act as a key barrier preventing the drivers from making shift to EVs. When the EV usage integrates with blockchain, it mitigates the limitation in charging station infrastructure along with the former problem discussed. The lack of trust exists between EV owners and charging station providers can be solved through secure and transparent payment processing possible by blockchain based smart contract. Building charging station on blockchain will ease the automated payment through the use of smart contract and create more efficient EV charging network. Also, the blockchain-based charging system would enable EV owners know if they are being charged in excess and Prosumer know if they are being underpaid. The high initial cost is another prominent issue within the market place. To address this issue the introduction of sharing economy to the EV industry showcases another innovative solution that blockchain offers. The blockchain enabled sharing economy platform allows individuals to access collaboratively with the prosumer and the consumer. This provides alternative to traditional ownership while reduces individual financial barriers and maximizing electric vehicle utilization across the network. The EV users have great opportunity worldwide to take a stake in the future of EV adoption on blockchain. Therefore, this work demonstrates the sharing economy while designing, building, and customizing smart contracts for prosumers and consumers by enabling decentralized payment systems. Our research aims to develop decentralized charging electronic payment systems using blockchain and customized smart contracts to build and design the application. For blockchain Solidity programming language is used. The application displays the charging process, payment system, and charging history information.</div></div>
Blockchain technology, characterized by its immutable, distributed ledger, has evolved significantly beyond its cryptocurrency origins, finding application in healthcare and organ donation systems. Specifically, Hyperledger Fabric emerges as a secure, enterprise grade solution for healthcare data management, with a primary focus on patient medical records. Traditional centralized storage of medical records poses challenges for patients, prompting the development of a Hyperledger Fabric-based system driven by smart contracts to enhance accessibility and security. In the realm of organ donation systems, blockchain is proposed as a remedy for the shortcomings of centralized models, offering heightened transparency and security. Notably, while previous solutions often leaned on Ethereum-based blockchains, this research pioneers the use of Hyperledger Fabric. Beyond organ donation, blockchain's attributes, including decentralization, transparency, and privacy, offer transformative potential in healthcare.
Recent developments in distributed ledger technology, artificial intelligence, and decision-making agents hold the promise of radically transforming market infrastructures. Indeed, the emergence of Autonomous Market Infrastructure (AMI)âan open, fully automated, and decentralized set of market-related functionalitiesâis widely anticipated. Such infrastructures, serving agents capable of fully autonomous behavior, would enable fully automated trading strategies. Moreover, as AMI-based solutions require minimal human intervention, they could be implemented at a fraction of existing costs. This should bolster competition and democratization, as AMI is accessible to everyone and establishes a level playing field.
Autonomous platforms for fintech, decentralized finance, and digital civil infrastructures are at the research frontier. Delivering on their promise requires a foundational approach. Future research and development directions are organised by core architectural principles, enabling technologies, major challenges and risks, methods for development and evaluation, and governance models. Autonomous economic interaction and decision-making are principally guided by policy goals. Independence from human involvement cannot be guaranteed, especially when external agents fulfil custodial roles, but risk can be mitigated by solidifying the foundations. The term âautonomous platformâ constitutes a composite of economic theory and systems design. Platforms support economic interactions enabled by information and communication technologyâin particular, the Internet. Their distinctive feature is an architecture composed of services provided by multiple stakeholders. Platform engineering is a design discipline that seeks to deliver the hoped-for benefits, including lower costs, greater selection, and novel business models, while mitigating risks such as fraud and the abuse of market power. The promise of autonomy stems from the deployment of becoming-type, human-compliant purpose design in an effective oversized-modular architecture and begins with the fulfilment of core architectural principlesâan autonomous, modular, and composable layer for economic interaction and decision-making.
Cryptocurrencies have started gaining ground as investment vehicles. Cryptocurrencies exhibit characteristics that differentiate them from traditional financial assets. In 2009, Bitcoin (BTC), the first digital currency, was launched. In 2021, the Securities and Exchange Commission (SEC) approved ProShares Bitcoin Strategy (BITO), the first U.S. Bitcoin futures exchange-traded fund (ETF). In 2024, SEC gave final approval for spot Ether (ETH) ETFs to start trading, further legitimizing the asset class. Although cryptocurrencies share many features of alternative assets, they are hindered by high volatility and regulatory uncertainties. Extant literature studies cryptocurrencies as alternative investments from various perspectives. Using market data, this empirical paper aims to contribute to the literature by studying the extent to which cryptocurrencies improve the risk-return profile of a diversified portfolio. Specifically, we do so by examining the economic impact of including Bitcoin for a passive investor investing in the U.S. Stock market index (S&amp;P 500 index).
The Paris Agreement of 2015 has prompted countries to accelerate their efforts to become carbon neutrality efforts, which meant reducing CO2 emissions to virtually zero. Limiting global warming to less than 1.5°C by 2050 rely on technologies that remove CO2 from the atmosphere faster than humans release it. This implies that CO2 will be removed at a rate of 1-30 gigaton per year by 2050. Carbon Capture Storage / Sequestration (CCS) and Utilization (CCU) are concepts and technologies that collect emitted CO2 store it permanently underground, or recycle it as energy or chemicals for use in manufacturing and other economic activities. CCS and CCU have been discussed globally, but have not reached local and practical levels. Currently planned large-scale CCS requires significant government investment and new technological developments for capture, transport, and storage / sequestration, therefore implementation is expected to start in the second half of 2030 towards the 2050 goal. The need to start acting now where possible rather than waiting for the distant future, makes it important to implement CCS and CCU on a small scale and build towards future scale-up as an immediate solution. This study proposes a support method and system to help companies that emit large amounts of CO2 such as power plants, cement, petrochemicals, and steel industries, to decide how to treat their CO2 emissions in the context of decarbonization. In this study, a Simple, Measurable, Attainable, Relative, and Time-Bound (SMART) decision support method and Direct Air Capture Location and Cost Simulator (DLCS) system were developed to provide a solution to the Negative Emission 5W1H âWhat, Who, Which, When, Why, and Howâ from the perspective of a company that emits CO2. A prototype model with parameter settings was proposed based on knowledge gained from practical experience. The functionality of the SMART method and DLCS system was confirmed by applying sample data from the actual data of the âTokyo Regionâ as a Proof of Concept (PoC). In this PoC, characteristics of direct air capture which is a critical technology for negative emissions, were verified. The core of the SMART and DLCS model entails combinatorial optimization, distance calculation, cost estimation, and market projection including constraint solution.
Raman Chadha, Paras Mahajan, Rajat Gupta, Sunil Khullar
The centralized nature of the existing digital identity infrastructure provides single points of failure and potential breaches of user privacy. This work proposes a decentralized infrastructure for managing digital identities using a permissioned blockchain and W3C-compliant Decentralized Identifiers and Verifiable Credentials together with zero-knowledge proofs to support a user-controlled and privacy-preserving identity verification process. The experiment verifies the ability of the proposed infrastructure to handle up to 155 transactions in the verification process in 105 ms for a maximum of 500,000 users without the possibility of egress operations as a potential threat to perform unauthorized access to the personal user information. User test participants are satisfied with the design of the wallet interface and the management of the corresponding consents. Process analysis indicates the feasibility of the designed infrastructure in complying with the key principles of the GDPR and CCPA regulations.
The rapid expansion of the blockchain gaming sector, projected to reach a $268.8 billion valuation by 2025 1 , has been severely compromised by the proliferation of automated Sybil attacks and bot-driven economic manipulation. Traditional anti-bot measures, such as CAPTCHAs and behavioural analytics, are increasingly circumvented by advanced AI-driven scripts. This paper proposes a novel Context-Aware Reputation-Identity Hybrid (CRIH) framework that integrates biometric-backed Proof-of-Personhood (PoP) with decentralized reputation metrics. By leveraging World IDâs hardware-oracle verification and recursive Zero-Knowledge Proofs (ZKPs), the CRIH framework enables thrustless identity portability across Layer 2 (World Chain) and Layer 3 (Mythos Chain) architectures. We demonstrate that this tiered, risk-sensitive approach significantly reduces bot-driven inflation while preserving player privacy and minimizing onboarding friction.
Verifying that a compiled binary originates from its claimed source code is a fundamental security requirement, called source code provenance. Achieving verifiable source code provenance in practice remains challenging. The most popular technique, called reproducible builds, requires difficult matching and reexecution of build toolchains and environments. We propose a novel approach to verifiable provenance based on compiling software with zero-knowledge virtual machines (zkVMs). By executing a compiler within a zkVM, our system produces both the compiled output and a cryptographic proof attesting that the compilation was performed on the claimed source code with the claimed compiler. We implement a proof-of-concept implementation using the RISC Zero zkVM and the ChibiCC C compiler, and evaluate it on 200 synthetic programs as well as 31 OpenSSL and 21 libsodium source files. Our results show that zk-compilation is applicable to real-world software and provides strong security guarantees: all adversarial tests targeting compiler substitution, source tampering, output manipulation, and replay attacks are successfully blocked.
This is an extended appendix for an unpublished paper. It covers the use of a framework defined in that paper to prove the zero-knowledge of a few zero-knowledge proofs. The first example, covering 3-colourability, is justified and explained. The second, covering boolean circuit satisfiability, is simply given.
This paper proposes an AI-based trading framework that integrates supervised price forecasting with reinforcement learning (RL)-based decision-making. The objective is to enhance both profitability and risk management in cryptocurrency trading by equipping RL agents with forward-looking market information and risk-aware incentives. The proposed methodology follows a two-stage design. First, a univariate long short-term memory (LSTM) model generates 72 bitcoin price forecasts. These predictions are used to compute future technical indicators, which are combined with current market indicators to construct an enriched, forward-looking state representation. Second, an RL agent is trained in this environment using a novel long-term reward function that incorporates transaction costs, drawdown penalties, volatility penalties, and delayed rewards to promote stable and sustainable trading behavior. Four state-of-the-art RL algorithms (PPO, SAC, TD3, and A2C) are systematically evaluated over randomized 180-day episodes using hourly bitcoin data. The results demonstrate that the proposed agent consistently outperforms conventional buy-and-hold and moving average crossover strategies, achieving an average profit ratio of 32% and a Sharpe ratio of 1.34. These findings highlight the novelty and effectiveness of combining mid-term price forecasts, enriched technical states, and risk-aware RL training for robust cryptocurrency trading.
This chapter examines money laundering as the systematic process of disguising illicit proceeds to make them appear legitimate, focusing on how repeated laundering patterns reveal organized criminal networks. The analysis covers the three-stage process of placement (introducing illegal funds into financial systems), layering (obscuring origins through complex transactions), and integration (reintroducing laundered funds as legitimate assets). Key modus operandi variables include the use of shell companies, structuring transactions to avoid reporting thresholds, trade-based manipulation, and exploitation of cash-intensive businesses. The chapter provides comprehensive detection indicators across six categories: transaction monitoring, customer behavior, geographic patterns, digital assets, trade anomalies, and lifestyle inconsistencies. Emerging threats through decentralized finance (DeFi) platforms and non-fungible tokens (NFTs) demonstrate how criminals adapt to new technologies while maintaining recognizable operational patterns. A case study of a $263 million cryptocurrency laundering scheme illustrates how money laundering interconnects with broader criminal enterprises including cyber theft, fraud, and violent crime. The chapter emphasizes that effective pattern recognition requires analyzing multiple indicators in combination, tracking recurring variables across time and jurisdictions, and understanding that money laundering is rarely an isolated crime but rather the financial backbone enabling sustained criminal activity.
A Long Short-Term Memory (LSTM) neural network trained on hourly ETH/USDT market data from the Binance exchange is used in this study to examine short-term Ethereum price behavior. The proposed model emphasizes learning temporal dependencies and momentum-driven structures rather than relying on conventional linear forecasting assumptions, acknowledging the highly nonlinear and noise-dominated nature of cryptocurrency markets. The daily high price of Ethereum is selected as the target variable in the forecasting task, which is defined as a univariate regression problem. To ensure realistic predictive assessment, model performance is evaluated using a strictly out-of-sample testing methodology. Empirical findings demonstrate that the LSTM model achieves a strong statistical fit despite significant market volatility. The obtained resultsâRMSE of 127.33, MAE of 98.76, MSE of 16,213.76, MAPE of 2.73%, and an R² of 0.96âindicate that a substantial portion of short-term price volatility is effectively captured by the nonlinear architecture. Even in a noise-dominated market, the low MAPE and high coefficient of determination suggest robust predictive alignment. Forecasts over the next five days reveal a recurring short-term directional pattern accompanied by widening prediction intervals, which reflect increasing uncertainty as the forecast horizon extends. This pattern underscores the intrinsic difficulty of achieving accurate price-level forecasts in highly volatile cryptocurrency markets. Overall, when applied to short-term cryptocurrency price dynamics, the results indicate that LSTM models are well-suited for capturing trend persistence and regime-related signals, affirming their usefulness as risk-aware decision-support tools rather than deterministic forecasting systems.
Liquidation of collateral are the primary safeguard for solvency of lending protocols in decentralized finance. However, the mechanics of liquidations expose these protocols to predatory price manipulations and other forms of Maximal Extractable Value (MEV). In this paper, we characterize the optimal liquidation strategy, via a dynamic program, from the perspective of a profit-maximizing liquidator when the spot oracle is given by a Constant Product Market Maker (CPMM). We explicitly model Oracle Extractable Value (OEV) where liquidators manipulate the CPMM with sandwich attacks to trigger profitable liquidation events. We derive closed-form liquidation bounds and prove that CPMM transaction fees act as a critical security parameter. Crucially, we demonstrate that fees do not merely reduce attacker profits, but can make such manipulations unprofitable for an attacker. Our findings suggest that CPMM transaction fees serve a dual purpose: compensating liquidity providers and endogenously hardening CPMM oracles against manipulation without the latency of time-weighted averages or medianization.
We present Viturka, a blockchain architecture that replaces wasteful proof-of-work mining with productive federated learning. The core innovation is Proof of Credibility (PoC): a consensus mechanism where block production probability is determined by accumulated reputation from validated AI contributions rather than computational hash power or financial stake. Viturka leverages recent breakthroughs in Zero-Knowledge Machine Learning (ZKML) to achieve cryptographic verification of model training. Validators generate zero-knowledge proofs attesting to correct training execution, enabling instant on-chain verification without trusted intermediaries or statistical consensus mechanisms. By integrating frameworks like EZKL and Lagrange's DeepProve with GPU-accelerated proving via the Icicle library, validation that previously required hours of recomputation now produces mathematical proofs verifiable in milliseconds. Participants earn credibility by contributing quality training data or validating others' contributions. Only the top 10 highest-credibility validators can participate in validation rounds, with mandatory cooldown periods ensuring rotation. The system uses a temporal commit-reveal scheme for data contributions combined with ZK proofs for validationâfake contributions result in permanent bans, while fraudulent validation is mathematically impossible. This creates infrastructure for training AI models on distributed data without central coordination, with economic incentives aligned toward data quality rather than raw computation. Applications range from commercially valuable use cases like DeFi credit scoringâwhich could unlock over $100B in overcollateralized capitalâto public-good AI for rare diseases, minority languages, and environmental monitoring.
SATHISHKUMAR RANGANATHAN, Muralindran Mariappan, M. Karthigayan
Swarm robotics is an emerging field capable of accomplishing complex tasks through collective behaviour. However, it continues to face persistent challenges in secure communication, decentralized decision-making, and scalability. To operate effectively in resource-constrained environments, swarm networks require a decentralized mechanism that is secure, fast, and efficient. Although many studies have explored the use of blockchain technology for swarm robotics, existing blockchain consensus algorithms such as Proof of Work (PoW), Proof of Stake (PoS), and their variants remain unsuitable due to high computational complexity and risk of stake centralization. To address these challenges, we introduce the blockchain-based Rotational Leadership Role (RLR) consensus algorithm, a voting-based consensus re-engineered from the Raft approach, together with Decentralized Task Authorization and Validation (DeTAV), a token-based mechanism for context-aware task validation. This design ensures efficiency, security, and scalability in swarm robotics and drone systems. RLR is lightweight and well suited to operate within the limited computing resources of small robots or aerial drones. To validate its performance, a custom-built robotic simulator was developed as part of this research. Experiments conducted with up to 70 concurrent robots demonstrated that RLR consumed under 90 MB Random Access Memory (RAM) and 12% Central Processing Unit (CPU), whereas PoW required 460 MB RAM and 27% CPU with a minimum difficulty level of 21, reflecting an 80% reduction in memory usage and a 55% reduction in CPU consumption. Scalability tests with 4 to 70 robots further revealed RLRâs scalability with an average of 78% higher throughput, 47% lower election latency, and 34% lower consensus latency. Additionally, under the simulated attack scenarios and assuming uncompromised cryptographic keys, DeTAVâs context-based validation consistently achieved 100% success in detecting and isolating Byzantine nodes, while reducing Quality of Detection (QoD) time by 67%. Collectively, these results confirm that RLR with DeTAV effectively meets the efficiency, security, and scalability requirements of swarm robotic and drone networks.
In order to ensure the transparent and immutable maintenance of healthcare data, Blockchain technology has been proposed, but it places privacy against core requirementsâone of which is privacy demanded by law. Anonymization techniques present today are useful in providing privacy, however they fall short in this sense in terms of guarantee. While zero-knowledge proofs (ZKPs) are one of the strongest cryptographic attestations, they come with hefty computational costs. This paper proposes an HH-ZKP model within a patient-specific-scope and selective disclosure on a smart contract. On a preliminary note, energetic sorting of electronic health records is actually done where anonymization is achieved using hybrid heuristic methods adhering to the constraints of k-anonymity and l-diversity. This is later followed by succinct ZKPs, ensuring that privacy is being obeyed without exposure of any of the hiding values. Again, the aggregations of these proofs are put onto the block with the smart contract, optimized for gas usage, to increase the scalability to a higher level. It is shown through experimental evaluations on the 10 K synthetic EHR dataset that the proposed scheme shows about a 75% reduced on-chain cost four times reduced proof sizes fully meeting HIPAA Safe Harbor compliance. The HH-ZKP model, by yielding a hybrid of heuristic-preprocessing and formal-principled cryptographic verification, is paving the way for scalable and regulator-aware blockchain health applications.
Guilhem Repetto, Nojan Sheybani, Gabrielle De Micheli, Farinaz Koushanfar
Privacy concerns in machine learning systems have grown significantly with the increasing reliance on sensitive user data for training large-scale models. This paper introduces a novel framework combining Probably Approximately Correct (PAC) Privacy with zero-knowledge proofs (ZKPs) to provide verifiable privacy guarantees in trustless computing environments. Our approach addresses the limitations of traditional privacy-preserving techniques by enabling users to verify both the correctness of computations and the proper application of privacy-preserving noise, particularly in cloud-based systems. We leverage non-interactive ZKP schemes to generate proofs that attest to the correct implementation of PAC privacy mechanisms while maintaining the confidentiality of proprietary systems. Our results demonstrate the feasibility of achieving verifiable PAC privacy in outsourced computation, offering a practical solution for maintaining trust in privacy-preserving machine learning and database systems while ensuring computational integrity.