The Hedera hashgraph algorithm has been shown to be Asynchronous Byzantine Fault Tolerant (ABFT) for achieving consensus on adding a transaction into local copies of a hash-graph distributed database. The ABFT result is theoretically the best result that can be achieved for distributed ledger technology (DLT) regarding trusting that the data in each local copy of a distributed global database has not been tampered with during each transaction process to add data into the global distributed database. The hashgraph algorithm ensures that each transaction in each local copy of the global database can be trusted to be a true copy of the data submitted by each node in the set of peer nodes as long as no more than 1/3 of the peer nodes in the peer-to-peer network of hashgraph nodes have been compromised. The Aquaticus, capture the flag (CTF) force-on-force free-play competition between Artificial Intelligence (AI)/Machine Learning (ML) agents enables use of a variety of ML algorithms to build AI/ML agents to play and win the CTF game in a maritime environment by employing the MOOS-IvP autonomy stack. This paper explores the integration of Hedera hashgraph DLT into the MOOS-IvP Aquaticus testbed for efficient and secure data transfer in collaborative autonomy scenarios. The study focuses on developing a multi-node Hedera network to support decentralized, real-time, and tamper-proof communication among autonomous agents in adversarial maritime environments. A detailed network setup using Docker and solo-compose is outlined, including transitioning from single-node to multi-node configurations. The system's application is evaluated in the context of the Aquaticus capture-the-flag (CTF) environment, highlighting its role in synchronizing flag positions and tagging status among unmanned surface vehicles (USVs), Initial findings indicate that the Hedera network can enhance data integrity and scalability while reducing latency in distributed systems. Challenges in scaling and resource optimization are discussed, along with proposed future work to deploy physical nodes using Raspberry Pi and integrate reinforcement learning frameworks like PyQuaticus. This research provides a foundation for advancing decentralized communication in autonomous robotics, emphasizing its potential for secure and robust multi-agent collaboration.
The decentralized finance (DeFi) ecosystem continues to evolve, allowing crypto holders greater control over their assets. This research examines key aspects of token accessibility, liquidity provisioning, and holder distribution. The study focuses on evaluating whether holders can check their ranking and percentage ownership, the availability of the token on decentralized exchanges (DEXs), the feasibility of liquidity pool creation, and opportunities for holders to acquire at least 0.1% of the total supply. In present paper, Coredaovip token has been considered as example to evaluate the crypto holder accessibility, liquidity and participation in decentralized ecosystem.
Hina Binte Haq, Syed Taha Ali, A. G. Sal'Man, Patrick McCorry · 5 authors
The Bitcoin mempool plays an integral role in transaction processing and propagation through the network. Frequent transaction congestion events, as well as spam and dust attacks can clog the mempool, leading to dropped transactions, processing delays, and increased transaction fees. Moreover, increasing transaction loads on the network result in higher resource costs to operate full nodes, thereby restricting Bitcoin's network footprint and negatively impacting its overall health and performance. In this paper, we present Carbyne, a novel mempool optimization scheme, which uses counting bloom filter constructions to adapt to increased transaction flows, thereby making nodes resilient to congestion and spam and dust attacks. We implement Carbyne in C++ and benchmark its performance using a novel data set of Bitcoin mempool activity over a 90-day period. We dramatically reduced the mempool's memory consumption by up to two orders of magnitude (from 300 MB to 3 MB) while verifying and forwarding transactions with 99.9% fidelity and a slight increase in computational load. We simulate extensive spam attacks on Carbyne and demonstrate that mempool loads of 1 GB can be accommodated in as little as 10 MB. Carbyne does not necessitate a hard fork, it will help deploy high-functioning nodes on resource-constrained platforms, and it may also be adapted to other cryptocurrencies.
N. Hafidi, Zakaria Khoudi, Mourad Nachaoui, Soufiane Lyaqini
Accurate cryptocurrency price forecasting is crucial for investors and researchers in the dynamic and unpredictable cryptocurrency market. Existing models face challenges in incorporating various cryptocurrencies and determining the most effective hyperparameters, leading to reduced forecast accuracy. This study introduces an innovative approach that automates hyperparameter selection, improving accuracy by uncovering complex interconnections among cryptocurrencies. Our methodology leverages deep learning techniques, particularly Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, in conjunction with the Genetic Algorithm (GA) to optimize hyperparameters. We propose and compare two architectures, LAO and LOEE, utilizing these methods to enhance forecast accuracy and address the challenges of the cryptocurrency market. This cutting-edge approach not only improves forecasting capabilities but also provides valuable insights for managing cryptocurrency investments and conducting research. By automating hyperparameter selection and considering interconnections between cryptocurrencies, our approach offers a practical solution for accurate cryptocurrency price prediction in a dynamic market environment, benefiting both investors and academics.
We present the design and implementation of a fourth-year undergraduate computer science elective course on cryptocurrency. The course introduces the basics of cryptocurrencies and blockchains using Bitcoin. Programming distributed applications for blockchains is explored using Ethereum via the Solidity programming language. Advanced theoretical concepts are discussed, using different cryptocurrencies to demonstrate how they are implemented in practice. Cryptocurrency applications are presented, along with a responsible examination of present-day cryptocurrency scams. The assignments build upon each other, with the students implementing a web-based blockchain application by the end of the semester. The course was well received, and student feedback was positive. All of the course materials are available online under public copyright licenses.
The rise of cryptocurrencies and decentralized fi- nance (DeFi) has highlighted the importance of secure and collaborative management systems for digital assets. Multi-party crypto vaults provide a way to ensure distributed control, privacy, and fault tolerance by involving multiple participants in the management and approval of transactions. This paper explores the two primary approaches—Multi-Signature (Multi-Sig) and Multi-Party Computation (MPC)—that underpin these vaults. Multi-Sig schemes offer fast, scalable solutions for real-time applications, while MPC ensures strong privacy and security by allowing encrypted computations without exposing private keys. In addition key management models, such as split-key and blockchain-based methods, and fault tolerance mechanisms, including social recovery and time-locked protocols, which ensure that vault systems remain secure and operational even in cases of participant failure. This paper recommends MPC as the optimal approach for high-security, privacy-sensitive applications, such as institutional custody and financial systems, while outlining av- enues for future research, including post-quantum cryptography and latency reduction in MPC protocols. Index Terms—Multi-Party Computation (MPC), Multi- Signature (Multi-Sig), Crypto Vaults, Threshold Cryptography, Blockchain Key Management, Decentralized Finance (DeFi),, Threshold ECDSA, Social Recovery Mechanism, Time-Locked Withdrawal Protocol, Fault Tolerance in Cryptography, Split- Key Management, Smart Contracts, Privacy-Preserving Cryp- tography, Institutional Crypto Custody
This chapter first introduces the concepts of currency, money, fiat money, and legal tender before positioning the various types of digital and virtual assets. As there is no universally accepted framework for classification of digital assets, this chapter will provide the reader with a simple-to-understand categorisation for virtual currencies and cryptocurrencies and position e-money and central bank digital currencies (CBDCs) in the mix (the latter being covered in Chapter 7 ). A classification framework will also be given for the various types of crypto-assets, covering cryptocurrencies, stablecoins (collateralised and non-collateralised), utility tokens (including exchange and governance tokens), security tokens (equity, asset backed, and debt collateralised), and non-fungible tokens (NFTs). It will detail the characteristics of all digital assets and offer comparisons with centralised payment ecosystems on aspects including use cases (current and potential), settlement, safety, privacy, risks, and many more.
This paper proposes a method of emulation of \verb|OP_RAND| opcode on Bitcoin through a trustless interactive game between transaction counterparties. The game result is probabilistic and doesn't allow any party to cheat, increasing their chance of winning on any protocol step. The protocol can be organized in a way unrecognizable to any external party and doesn't require some specific scripts or Bitcoin protocol updates. We will show how the protocol works on the simple \textbf{Thimbles Game} and provide some initial thoughts about approaches and applications that can use the mentioned approach.
Ben Biedermann, Matthew Scerri, Victoria Kozlova, Joshua Ellul
Web3’s decentralised infrastructure has upended the standardised approach to digital identity established by protocols like OpenID Connect. Web2 and Web3 currently operate in silos, with Web2 leveraging selective disclosure JSON web tokens (SD-JWTs) and Web3 dApps being reliant on on-chain data and sometimes clinging to centralised system data. This fragmentation hinders user esxperience and the interconnectedness of the digital world. This article explores the integration of Web3 within the OpenID Connect framework, scrutinising established authentication protocols for their adaptability to decentralised identities. The research examines the interplay between OpenID Connect and decentralised identity concepts, the limitations of the existing protocols like OpenID Connect for verifiable credential issuance, OpenID Connect framework for verifiable presentations, and self-issued OpenID provider. As a result, a novel privacy-preserving digital identity bridge is proposed, which aims to answer the research question of whether authentication protocols should inherently support Web3 functionalities and the mechanisms for their integration. Through a Decentralised Autonomous Organisation (DAO) use case, the findings indicate that a privacy-centric bridge can mitigate the existing fragmentation by aggregating different identities to provide a better user experience. While the digital identity bridge demonstrates a possible approach to harmonise digital identity across platforms for their use in Web3, the bridging is unidirectional and limits root trust of credentials. The bridge’s dependence on centralised systems may further fuel the debate on (de)centralised identities.
This paper introduces 3MEthTaskforce (https://3meth.github.io), a multi-source, multi-level, and multi-token Ethereum dataset addressing the limitations of single-source datasets. Integrating over 300 million transaction records, 3,880 token profiles, global market indicators, and Reddit sentiment data from 2014-2024, it enables comprehensive studies on user behavior, market sentiment, and token performance. 3MEthTaskforce defines benchmarks for user behavior prediction and token price prediction tasks, using 6 dynamic graph networks and 19 time-series models to evaluate performance. Its multimodal design supports risk analysis and market fluctuation modeling, providing a valuable resource for advancing blockchain analytics and decentralized finance research.
Abstract The rapid growth in the speed and convenience of information dissemination has made copyright infringement increasingly common. Blockchain technology solves pain points such as difficulties in traditional copyright registration, easy infringement, and difficulties in confirming and safeguarding rights. It also realises the decentralised management of copyright, network-wide tracking and monitoring, trusted certificate deposits, among others. However, the efficient original authentication of works and the function of blockchain to create copyright trading channels in the field of copyright are often ignored. This paper designed a self-adaptive learning similarity detection fusion strategy to protect the copyright of original digital works, namely SAAChain, and built a platform for releasing and storing original works based on non-fungible tokens. SAAChain first measures the similarity of a work based on adaptive learning to realise the originality authentication of works. Secondly, the works are stored on the InterPlanetary File System as NFTs, along with copyright information. Finally, a smart contract based on Ethereum and ERC-721 is designed to realise the free circulation of digital rights while simultaneously constructing an efficient and convenient digital rights protection system. Experiments show that the accuracy of the fusion strategy for adaptive work similarity detection can reach above 97%, which meets the requirements of work originality verification. Because of the storage mode of the platform, the system has good performance in terms of response speed and storage efficiency. The entire process provides a full-process and transparent transaction platform for all parties and guarantees the copyright ownership of works as well as the non-tampering and traceability of copyright information.
Advanced Data Storage Technologies
Computer Graphics and Visualization Techniques
Advanced Steganography and Watermarking Techniques
The Federated Unified Secure Exchange (FUSE) algorithm represents a breakthrough in computer science and cybersecurity, building upon three foundational elements: Homomorphic Encryption, Zero-Knowledge Proofs, and Federated Learning. By deeply integrating these three pillars, FUSE not only guarantees data integrity and privacy in decentralized environments but also maintains high performance and remarkable scalability. This paper details the architecture, mathematical principles, and real-world applications of FUSE across Internet of Things (IoT), healthcare, finance, and supply chains. In addition, it compares FUSE with several “classic” algorithms that have dramatically changed the world—such as QuickSort, RSA, Dijkstra, PageRank, FFT, Backpropagation, SHA, Monte Carlo, Gradient Descent, and Blockchain—highlighting how FUSE addresses existing limitations in data protection and distributed computation.
This thesis presents novel techniques to improve the efficiency, scalability, and security of Data Confidence Fabrics (DCFs), a framework that ensures data authenticity and integrity in large scale, heterogeneous distributed systems by generating metadata at each point of data formation, processing, and transmission. Despite their strengths, DCFs face significant challenges, including excessive annotation and transactional overhead, which reduce scalability and efficiency, and metadata privacy risks, which compromise sensitive network information. To address these challenges, this research proposes methods that improve system scalability, enable efficient annotation retrieval, and protect sensitive network information, with a focus on the Alvarium Data Confidence Fabric, though the solutions are broadly applicable to other DCFs. A primary contribution of this work is addressing the efficiency and scalability challenges by reducing annotation overhead through compact annotation techniques, particularly annotation batching. By aggregating multiple annotations into a single ledger transaction, this approach minimizes redundancy, storage costs and ledger interactions. However, batching introduces complexity in retrieving individual annotations. To overcome this, two retrieval methods are proposed: Batch Keys, which use mapping tables to quickly locate individual annotations based on a Batch key, and Bloom Filters, which provide a low-overhead approach for efficiently verifying the presence of annotations. Another major focus of this work is mitigating metadata privacy risks, where adversaries could analyze annotations to infer network structures. To obscure network patterns, two privacy-preserving schemes, Hostname Mapping and Hostname Encryption, are introduced, with Hostname Encryption offering a more efficient and secure alternative. Additionally, the research highlights how timestamp metadata can be exploited to reconstruct network structures through clustering techniques. To mitigate this vulnerability, a timestamp obfuscation solution is proposed, introducing controlled randomness to disrupt predictable timing patterns and protect network confidentiality. In summary, the thesis introduces and evaluates methods that significantly enhance the efficiency, scalability, and security of DCFs. These contributions strengthen the practical deployment of DCFs in cloud-edge environments and provide a foundation for future research in secure and trustworthy data management across distributed systems.
The reproducibility of scientific simulations is one of the key challenges of scientific research. Current best practices involve version-controlled code, tracking dependencies, specifying hardware configurations, and sometimes using Docker containers to enable one-click simulation setups. However, these approaches still fall short of achieving true reproducibility. For example, Docker depends on the underlying host kernel, and high-performance computing (HPC) codes often link with specific kernel modules and headers. Over time, changes in host kernel versions can render Dockerized simulations unusable. Furthermore, non-deterministic simulations, such as Monte Carlo methods, may not yield identical results even when rerun on the same hardware with the same code.This talk explores the potential of blockchain technology to address these challenges. By running simulations natively on-chain (via smart contracts) and emitting logs of each state transition, we can achieve reproducibility while also verifying the simulation's authenticity (associating the original author of the simulation and the reporting author).Other potential ideas include using zero-knowledge proofs to hash the call stack and the stack memory into a Merkle tree or also to think about the tokenisation of compute.We will delve into the technical feasibility and potential benefits of this approach, including its implications for trust, transparency, and the future of scientific research.
Natalia Borgoñós García, María Hernández Padilla, Antonio Fernando Skarmeta Gómez
Abstract Data Spaces are ecosystems designed to allow multiple organizations or companies to share data in a secure manner. Despite the potential of these technologies, they encounter a number of challenges and privacy issues that limit their use. Privacy Preserving Enablers are mechanisms developed to tackle these difficulties, ensuring data integrity and access control. This paper aims to analyze the role of some Privacy Preserving Enablers and its integration with Connectors in the context of Data Spaces. The research will focus on key enablers, including a Self-Sovereign Identity with Zero-Knowledge Proof, which is a privacy preserving approach that allows users to verify their identity and attributes without the need to disclose underlying data, ensuring their privacy. Additionally, the usage of Sticky Policies instantiated through Attribute-Based Encryption attaches control policies into the encrypted data in order to have an attribute-based access control, enhancing its security. The application of Policies Enforcement assure the consistent application of policies and the maintenance of the security within the Data Space.
Senthil G. A, R. Prabha, R. Avudainayaki, Srinidhi Sundaram · 5 authors
Due to the rise of cryptocurrencies and blockchain technology, banking is changing forever through introducing transparent, secure, and decentralized solutions. They strongly rely on solid mathematics to ensure the security and functionality of these technologies. This paper explores the importance of mathematics in blockchain and cryptocurrency systems. In this article, we take a look at the properties of cryptographic hash functions which are necessary for tamper–evidence data integrity and blockchain records immutability. Next, we dive into public-key cryptography and digital signatures, which are core to the security of transactions and user authentication. These aspects are essential to maintaining secure communication and identity verification within the blockchain realm. So not PoW—because everyone knows PoW now—we go to another method of consensus such as a PoS, for example. With PoS, validation is dependent on ownership of bit coin rather than the vastly voracious computational power that PoW is reliant upon, thus making PoS more energy efficient and opening a wider net in terms of network participation. It discusses about the role of error-correcting codes in blockchain systems, and why they are needed to ensure that data is protected from unintentional corruption or deliberate attacks. These codes guarantee information saved on the blockchain has reliability and sustainability. These methods also include mathematical modeling and game theoretic applications to blockchain design that study participant behavior, promote cooperative behaviors, and deter malicious actions. In the world of cryptocurrency, these mathematical principles have particular applications; cryptographic techniques are used by consensus mechanisms and smart contracts to ensure privacy in decentralized and secure transactions. It is a powerful new area of mathematics that offers exciting possibilities for blockchain applications particularly for privacy-preserving transactions and computations. We provide a comprehensive overview of these essential mathematical concepts in order to provide developers, investors, and policymakers an insight into the underlying technology that sustains blockchain and cryptocurrency systems. All of which allows stakeholders to confidently orient themselves in this fast-emerging area. Lastly, we baptize some path-breaking directions for blockchain, including homomorphic encryption which enables evaluating expressions on ciphertexts.
Ensuring the reproducibility of scientific simulations is a persistent challenge, despite current best practices like version control and containerization. Factors such as floating-point arithmetic variations, hardware differences, and concurrency issues often prevent bit-for-bit replication of results. This paper investigates the techniques that distributed ledger technologies employ to achieve deterministic computations and application of these techniques to enhance the reproducibility, trustworthiness and verifiability of scientific simulations. We explore two primary approaches: executing simulations directly “on-chain” for complete transparency and deterministic replay, and performing computations “off-chain” while anchoring their integrity to a blockchain via cryptographic proofs, such as Zero-Knowledge Proofs (ZKPs) and Merkle trees.
Christos Karapapas, Iakovos Pittaras, George C. Polyzos, Constantinos Patsakis
The InterPlanetary File System~(IPFS) offers a decentralized approach to file storage and sharing, promising resilience and efficiency while also realizing the Web3 paradigm. Simultaneously, the offered anonymity raises significant questions about potential misuse. In this study, we explore methods that malicious actors can exploit IPFS to upload and disseminate harmful content while remaining anonymous. We evaluate the role of pinning services and public gateways, identifying their capabilities and limitations in maintaining content availability. Using scripts, we systematically test the behavior of these services by uploading malicious files. Our analysis reveals that pinning services and public gateways lack mechanisms to assess or restrict the propagation of malicious content.