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51,074 results · page 181 of 2,129
Davide Mancino, Luca Pennella
No abstract is available for this record.
David Krause
No abstract is available for this record.
Badr Farih
No abstract is available for this record.
Milan Jevtić
Digital assets have become a significant and indispensable part of the modern financial system and have brought innovations in the areas of payments, investments, and financial intermediation. However, their expansion brings numerous regulatory challenges, particularly with regard to preventing money laundering, user identification, the legal treatment of decentralized finance, and privacy protection. Approaches to the regulation of digital assets vary significantly among jurisdictions - while some countries introduce comprehensive legislation, others apply restrictive or fragmented policies. Serbia has positioned itself as one of the first countries in the region to adopt a specific Law on Digital Assets (2020), thereby establishing a regulatory framework for this market. This paper analyses the legal challenges of digital assets, exploring national and international regulatory approaches, including the European Union's MiCA Regulation. It also examines the need to strike a balance between fostering innovation and ensuring the stability of the financial system. The key finding is that continuous international cooperation and a flexible regulatory framework are necessary to enable the sustainable development of digital assets and the technologies that support them.
Davide Sperolini
No abstract is available for this record.
Zining Wang, Jiaxin Duan, Fangyuan Kou
No abstract is available for this record.
Steven Paul Nohr
<b><i>Liquidity Exit Drain</i></b>, also referred to as a <b><i>Rug Stream</i></b>, describes a class of gradual capital extraction attacks in decentralized finance (DeFi) and stablecoin ecosystems. Unlike abrupt rug pulls or discrete exploit-based failures, Liquidity Exit Drains operate through prolonged, cumulative actions that remain within protocol-defined operational and governance boundaries. By exploiting trust persistence, governance opacity, and parameter flexibility, these attacks systematically siphon liquidity over extended time horizons while evading conventional audits and user detection. This paper formalizes <b><i>Liquidity Exit Drain</i></b> as a distinct economic attack class, examines its structural enablers and execution patterns, and outlines why existing security, audit, and monitoring frameworks frequently fail to detect it. The analysis highlights the need for longitudinal economic oversight and governance-aware risk assessment beyond static code correctness.
Niederwolfsgruber Georg
No abstract is available for this record.
Богдан Петрович Віннічук
Кваліфікаційна робота бакалавра складається зі вступу, чотирьох розділів, висновків, списку використаних джерел та додатків. Перший розділ кваліфікаційної роботи присвячено дослідженню актуального стану досліджуваної проблематики, обґрунтуванню обраного напряму роботи, а також визначенню та відбору методів і засобів проектування. У другому розділі розглянуто та обґрунтовано підходи й технології для вирішення поставлених завдань. Також наведено функціонально-структурну схему об’єкта проектування та ER-діаграму бази даних. Третій розділ охоплює безпосередню розробку програмного забезпечення та практичне втілення проєктованого об’єкта. Четвертий розділ містить SEO-оптимізацію інформаційно-комп’ютерної системи. У висновках підсумовано результати, отримані в ході виконання всіх попередніх розділів роботи.
Choonhwa Lee, Yibo Zhang, Dani Mertens, Eunsam Kim
Decentralized workflows supported by provenance data aim to combine into a platform with high trustworthiness, transparency, and accountability. Despite the huge potential of the approach, one limitation of the system architecture is that it lacks access to external data, limiting the functionality and potential use cases of the workflow. To address this limitation, we extend an existing decentralized workflow with a provenance bridge, enabling the workflow to access external provenance-supported data without damaging the trustworthiness of the system. The introduction of this bridge effectively broadens the range of use cases for the decentralized workflow by opening it to various external provenance-supported data sources. This article presents the architectural design of the provenance bridge-enabled workflow system, and discusses our prototype implementation effort along with evaluation results. Specifically, we have designed and implemented the architectural framework of the provenance bridge that expands the provenance domain boundary by incorporating support for provenance model translation and oracle-based external data access. The proposed approach lays a solid foundation for a wider adoption of smart contract-based workflow systems by effectively bridging the gap between different provenance domains.
Aditya Gupta, Vibha Jain
ABSTRACT The perishable food cold chain is a vital part of the global food system, mainly ensuring the quality of temperature‐sensitive products such as milk, seafood, fruits, and vegetables. However, this system still faces several challenges related to real‐time monitoring, data transparency, and proactive demand planning, which often lead to spoilage, food safety violations, and disruptions. In this work, we propose a comprehensive end‐to‐end system that includes Internet of Things (IoT)‐based environmental sensors, blockchain‐enabled immutable logging, and a hybrid ARIMA–LSTM model for demand forecasting and spoilage risk detection. The system uses ESP32 microcontrollers with DHT11 sensors at key cold‐chain points to collect temperature and humidity data, streamed to Blynk dashboards for real‐time visualization and anomaly alerts. Anomalies and product metadata are permanently recorded through smart contracts on a private Ethereum blockchain, while payloads are stored off‐chain on IPFS for traceability and auditability. Additionally, historical blockchain logs and external sales data are used to train a hybrid ARIMA–LSTM model that predicts future demand and spoilage risks more accurately. Experimental results show that the proposed model achieves a forecasting accuracy of 90% ( R 2 = 0.90), outperforming baseline approaches on multiple metrics including RMSE, MAE, MAPE, and R 2 . The framework is scalable and data‐driven, aiming to improve supply availability and reduce food waste.
Boon Chuan Lim
No abstract is available for this record.
Robin Schattmann
No abstract is available for this record.
Hugo Muñoz Ureña
No abstract is available for this record.
Saeed Ahmadi
No abstract is available for this record.
Prof. M. A. Sayyad, Veerendra Yadav, Dr. Geetika M. Patel, Dr. Prakash Deep · 8 authors
The problem of data privacy, interoperability, cyberattacks, and unauthorized changes of sensitive medical records are becoming critical issues in healthcare information exchange systems. The conventional centralized healthcare designs have single-point failures, inadequate transparency, sluggish data synchronization, and insufficient trust management among dispersed medical organizations. In order to overcome these shortcomings, this paper suggests a Blockchain-Assisted Distributed Artificial Intelligence Framework to Secure Healthcare Information Exchange and Data Integrity. The suggested architecture combines a distributed AI-based healthcare analytics system with blockchain-based immutable ledger systems to provide secure, open, and alteration-free medical data exchange among various healthcare nodes. The automated access control and the secure management of authorization is applied using smart contracts, and intelligent anomaly detection and integrity verification of healthcare transactions are implemented using distributed AI modules. The framework also includes encrypted communication and decentralized consensus systems to promote security and reliability in the context of multi-institutional healthcare settings. Simulated healthcare data based on experimentation shows that the proposed framework has a data integrity verification accuracy of 96.4, anomaly detection accuracy of 92.7 and offers both efficient and secure transaction validation performance at a ratio of 41.3 lower than traditional centralized healthcare systems. The suggested architecture enhances the security of healthcare data and trust management, scalability and interoperability of the next-generation intelligent healthcare ecosystems significantly.
Mojtaba Khalili
Quasi-adaptive non-interactive zero-knowledge (QA-NIZK) arguments are fundamental cryptographic primitives widely used in privacy-preserving technologies such as anonymous credentials, group signatures, e-cash, and blockchain-based applications. We present the first tightly secure unbounded simulation sound quasi-adaptive non-interactive zero-knowledge argument system from simple assumptions. The construction has a security loss ofO(1), a compact common reference string, constant size proofs, and its security relies on the hardness of the well-known SXDH assumption. Our result improves state-of-the-art (Couteau and Hartmann, CRYPTO 2020) in terms of the proof size (about three times), a lower security loss, and also with respect to the underlying hardness assumptions. The tight security reduction enables shorter key-length recommendations, leading to improved concrete efficiency. Our main technical contribution is a novel proof technique inspired by the randomization technique of the Naor-Yung double-encryption paradigm and the adaptive partitioning due to Hofheinz (EUROCRYPT 2017).
CS Chai
No abstract is available for this record.
Dikshant Agarwal
No abstract is available for this record.
David Krause
No abstract is available for this record.
David Krause
No abstract is available for this record.
Vanessa Villanueva Collao
No abstract is available for this record.
Osama Wagdi
No abstract is available for this record.
Siddharth Jain, Divyansh Jain
The pervasive volatility and structural complexity of decentralized assets present significant challenges for modern portfolio management. This paper introduces Coin Quest, a novel, high-fidelity cryptocurrency tracking and risk management platform designed to address critical shortcomings in existing market solutions, notably high data latency and the deficiency of robust quantitative risk tools. Our technical proposal mandates a resilient microservices architecture centered on Apache Kafka for high-throughput, low-latency data stream ingestion, ensuring real-time portfolio valuation across disparate exchanges and blockchains. The analytical core of Coin Quest implements the Monte Carlo Simulation (MCS) framework to compute Value at Risk (VaR) and the superior measure, Conditional Value at Risk (CVaR), recognizing the non-normal return distributions inherent to crypto assets. Furthermore, we detail specialized algorithms necessary for comprehensive tracking and valuation of complex Decentralized Finance (DeFi) positions, including the calculation of Impermanent Loss, and quantitative monitoring of NonFungible Tokens (NFTs) using floor price metrics. We conclude by outlining empirical validation requirements demonstrating the system’s capacity to maintain sub-100ms data latency and confirming the superior predictive accuracy of the MCS-based risk model against traditional historical simulations in highly volatile market environments.