Beth Probert, Ruaridh Clark, Erik Blasch, Malcolm Macdonald
As the density of objects in Low Earth Orbit continues to increase, greater levels of coordination among satellites are needed to ensure the continued operation and sustainability of the space environment. Existing centralised space governance frameworks suffer from delays, ambiguity, and vulnerability to manipulation, introducing further complexity and risk into satellite operations, and hindering the development of new space-based services. This review examines the feasibility of leveraging Distributed Ledger Technologies to facilitate secure, decentralised, and autonomous coordination among satellites, conceptualised as an extension of Internet of Things networks. By examining analogous applications in swarms of Uncrewed Aerial Systems, and the Internet of Things, key system requirements for decentralised satellite coordination are defined. It is found that traditional Blockchain systems using Proof of Work-based consensus mechanisms are impractical for use in space-based systems, whilst Directed Acyclic Graph architectures, Sharded Blockchain architectures, and non-deterministic Proof of Stake consensus mechanisms are the most suitable. Critical gaps preventing the implementation of Distributed Ledger Technologies in the space environment are identified. These include wider regulatory considerations, and technical obstacles relating to the storage of transactional data and reaching consensus in time-varying, asynchronous networks.
Zero-knowledge proof (ZKP) is a widely used privacy-preserving technology, where multiscalar multiplication (MSM) accounts for over 70% of the computational workload. The acceleration of MSM can enhance the overall performance of ZKP, making it a focal point of community attention. However, in practical applications involving the deployment of multiple MSM accelerators, existing designs often overlook strategies for optimizing bandwidth and area efficiency. To address this, we propose Myosotis, an efficiently pipelined and parameterized MSM architecture. By sharing input data and allocating cache effectively, it mitigates average transmission bandwidth in runtime. Myosotis also supports the use of multiple point addition (PADD) units to achieve performance gains, balancing area overhead and latency for improved area efficiency. Different parameter selection enables a tradeoff between the performance, area, and bandwidth of the MSM accelerator. When benchmarking with MSM degrees between$2^{18}$and$2^{26}$, our proposed baseline design achieves up to$3.32\times $and$6.72\times $speedups over state-of-the-art FPGA and ASIC designs. Compared to the baseline, Myosotis with two window MSMs and one PADD unit reduces bandwidth demand by 43% while maintaining similar area and latency. On the other hand, Myosotis with three window MSMs and two PADD units decreases latency by 43% and bandwidth by 17%, with only a 9% area increase.
Existing game tournaments have faced problems where large game companies or a small organizing committee monopolize decision-making on rules and revenue distribution, thereby hindering fairness and creativity.To overcome these limitations, this paper proposes a DAO (Decentralized Autonomous Organization)-based, broadcaster-centric operational model.In this model, all stakeholders in the tournament participate in transparent decision-making and revenue distribution through smart contracts, thereby enhancing fairness and trust.Furthermore, it manages copyright negotiations with game companies and oversees revenue models within the DAO, ultimately promoting ecosystem diversity and sustainability.Through conceptual simulations and case analyses, this study verifies the feasibility of the proposed model.Compared to traditional centralized structures, the findings confirm the model's potential for democratizing the decision-making process, establishing fair compensation frameworks, and proactively resolving copyright issues.
There have been many attacks threatening the security of LMD-GHOST, the consensus protocol adopted in Ethereum 2.0. Therefore, a consensus protocol which is provably secure is needed. The existing work "Goldfish" is provably secure but requires 3Δ rounds per slot, with Δ being the maximum network delay. In this study, we propose a provably secure protocol with a structure of 2Δ rounds per slot by parallelizing the block proposal phase and the voting phase in Goldfish. By reducing the number of rounds per slot, transaction processing speed increases, leading to enhancing scalability.
The UpOn platform leverages cutting-edge modular smart contract architecture to redefine Real World Asset (RWA) management by creating a legally compliant, decentralized ecosystem for virtual office investment, launching, and rental. Built with a focus on transparency, security, and compliance, UpOn allows users to seamlessly participate in real estate investment through Non-Fungible Tokens (NFTs) that represent virtual office spaces. By integrating law firms for legal oversight, UpOn ensures that all transactions align with global legal standards, providing users and investors with trust and security. The platform comprises four main components-Invest, Launch, Rent and Legal-each of which serves a unique function within the ecosystem. The Invest section enables investors to fund new office projects through NFT minting, while Launch allows owners of established offices to tokenize their assets. In the Rent section, users can securely lease office NFTs with contract-backed agreements tailored to local regulations, and the dashboard provides realtime data retrieval from smart contracts, ensuring a transparent user experience. Finally, the Legal component, backed by law firms, safeguards user rights and enforces compliance in all jurisdictions. With its modular design and elimination of centralized databases, UpOn showcases the full potential of Web3 by simplifying the traditional complexities of RWA projects, lowering costs, and offering unprecedented accessibility to global markets.
As Web3 gains momentum and ushers in the era of decentralized applications and blockchain technology, developers are faced with new challenges in designing robust and scalable architectures. Service Oriented Architecture (SOA) is a proven architectural pattern that provides a solution for building modular, interoperable, and scalable systems. In this chapter, we explore SOA in general, its benefits and challenges, and discuss how this architecture pattern can be applied to Web3 applications.
This study explores the integration of Plithogenic Hypersoft Sets (PHSS) in Multi-Criteria Decision Making (MCDM) for cryptocurrency investment analysis. Given the volatile and unpredictable nature of the cryptocurrency market, traditional decision-making models often fail to capture the complexities and uncertainties present. By incorporating the advanced concept of PHSS, which accommodates multiple membership degrees (fuzzy, intuitionistic fuzzy, neutrosophic), we propose a more robust framework for investment decision-making. The study normalizes cryptocurrency data and applies PHSS to a set of cryptocurrencies (e.g., BTC, ETH, BNB, SOL, and XRP), analyzing key attributes such as price changes, market cap, volume, and circulating supply. Our results demonstrate that PHSS can enhance the accuracy and reliability of financial decision-making, providing valuable insights for investors.
N Nalini, V. Nageshwara Rao, K. Somasekhara Rao, P. Shobha
This research explores Decentralized Finance (DeFi) through the lens of enhanced applications, introducing the concept of Decentralized Finance Enhanced (DeFine). It examines the transformative potential of DeFi, analyzing its evolution and the innovative solutions driving its progress. The objective is to investigate how DeFine addresses scalability, interoperability, security, and user experience challenges in the DeFi ecosystem. Through case studies and analysis, this proposed work provides insights into the implications, opportunities, and risks associated with the adoption and maturation of DeFi enhancements.
A. Jabbari, Gowri Ramachandran, Sidra Malik, Raja Jurdak
In the current digital landscape, supply chains have transformed into complex networks driven by the Internet of Things (IoT), necessitating enhanced data sharing and processing capabilities to ensure traceability and transparency. Leveraging Blockchain technology in IoT applications advances reliability and transparency in near-real-time insight extraction processes. However, it raises significant concerns regarding data privacy. Existing privacy-preserving approaches often rely on Smart Contracts for automation and Zero Knowledge Proofs (ZKP) for privacy. However, apart from being inflexible in adopting system changes while effectively protecting data confidentiality, these approaches introduce significant computational expenses and overheads that make them impractical for dynamic supply chain environments. To address these challenges, we propose ZK-DPPS, a framework that ensures zero-knowledge communications without the need for traditional ZKPs. In ZK-DPPS, privacy is preserved through a combination of Fully Homomorphic Encryption (FHE) for computations and Secure Multi-Party Computations (SMPC) for key reconstruction. To ensure that the raw data remains private throughout the entire process, we use FHE to execute computations directly on encrypted data. The "zero-knowledge" aspect of ZK-DPPS refers to the system's ability to process and share data insights without exposing sensitive information, thus offering a practical and efficient alternative to ZKP-based methods. We demonstrate the efficacy of ZK-DPPS through a simulated supply chain scenario, showcasing its ability to tackle the dual challenges of privacy preservation and computational trust in decentralised environments.
Securing interoperable and sovereign data exchange in the Industrial Internet of Things (IIoT) for machine data exploitation by third parties presents a significant challenge. This work addresses this by integrating IOTA Distributed Ledger Technology (DLT) with the International Data Spaces (IDS) Reference Architecture Model (RAM), creating a decentralized data space optimized for IIoT ecosystems. This research demonstrates the practical implementation of core IDS architectural concepts within the IOTA framework, overcoming theoretical DLT limitations and showcasing IOTA’s capability to enhance data sovereignty and interoperability in the IIoT, moving beyond traditional blockchains, which are constrained by scalability and efficiency issues. It sets the stage for future evaluations and broader applicability studies, paving the way for advancements in secure, sovereign, interoperable, and efficient data management.
In the context of Web3.0, the rapid pace of technological innovation has resulted in the emergence of novel computing paradigms. This is a natural result of the exponential growth of technology. Within the ecosystem of Web 3.0, the major emphasis of this research is on rethinking architecture and protocols in order to be ready for the revolution in edge computing. To improve the efficacy, security, and scalability of data processing and transmission, DECABI provides a dynamic edge computing architecture with integrated blockchain characteristics. The study accelerates data transfers without compromising their integrity by using dynamic resource allocation, encrypted data transmission, and a consensus protocol for edge computing (CPEC). The suggested method has much better performance than the alternatives. This exemplifies how the method has the ability to completely alter the way edge computing is carried out in the Web3.0 era. Countless simulations and exhaustive studies confirmed the existence of this option. This study lays the groundwork for a more stable and secure Web3.0 ecosystem, and it also makes a substantial contribution to the current conversation about the future of computer architectures and protocols.
Ring confidential transactions (RingCT) is a classical protocol that enhances transaction privacy within blockchain technology. It is primarily utilized in anonymous cryptocurrencies such as Monero. In recent years, to counter the security threats posed by quantum computing to classical RingCT (C-RCT), several post-quantum RingCT (P-RCT) protocols were proposed. However, prior studies do not address the account transition from C-RCT to P-RCT, which is essential to ensure the quantum resistance and usability of user accounts following the upgrade from C-RCT to P-RCT. In this article, we introduce a RingCT account transition protocol (RAT), which allows users to migrate their accounts from the blockchain with C-RCT to that with P-RCT. The protocol consists of two cryptographic components: 1) a zero-knowledge proof scheme used to prove that two group generators correspond to the same witness and 2) a cross-cryptosystems commitment transition protocol. RAT is fully decentralized and does not require any trusted third parties. Additionally, the transition process does not disclose the account balance and maintains both atomicity and consistency. The security of our protocol relies on the cryptographic components within RingCT, without the need for additional security assumptions, such as a trusted setup. The experimental results demonstrate the effectiveness of our protocol in practice.
Xudong Zhu, Haoqi He, Zhengbang Yang, Yi Deng · 6 authors
Zero-knowledge proof (ZKP) is a cryptographic primitive that enables a prover to convince a verifier that a statement is true, without revealing any other information beyond the correctness of the statement itself. Due to its powerful capabilities, its most practical type, called zero-knowledge Succinct Non-interactive ARgument of Knowledge (zkSNARK), has been widely deployed in various privacypreserving applications such as cryptocurrencies and verifiable computation. Although state-of-the-art zkSNARKs are highly efficient for the verifier, the computational overhead for the prover is still orders of magnitude too high to warrant use in many applications. This overhead arises from several time-consuming operations, including large-scale matrix-vector multiplication (MUL), number-theoretic transform (NTT), and especially the multi-scalar multiplication (MSM) which constitutes the largest proportion. Therefore, further efficiency improvements are needed.In this paper, we focus on comprehensive optimization of running time and storage space required by the MSM algorithm on GPUs. Specifically, we propose a novel, modular and adaptive parameter configuration technique—elastic MSM to enable us to adjust the scale of MSM according to our own wishes by performing a corresponding amount of preprocessing. This technique enables us to fully unleash the potential of various efficient parallel MSM algorithms. We have implemented and tested elastic MSM over three prevailing parallel Pippenger algorithms on GPUs. Across various preprocessing space limitations (across various MSM scales), our constructions achieve up to about 1.90×, 1.08× and 1.36× (2.58×, 1.39× and 1.91×) speedup versus three state-of-the-art parallel Pippenger algorithms on GPUs, respectively.From another perspective, elastic MSM could also be regarded as a preprocessing technique over the well-known Pippenger algorithm, which is modular and could be used to accelerate almost all the most advanced parallel Pippenger algorithms on GPUs. Meanwhile, elastic MSM provides an adaptive trade-off between the running time and the extra storage space needed by parallel Pippenger algorithms on GPUs. This is the first preprocessing technique to retain the improved MSM computation brought by preprocessing under varying storage space limitations. Specifically, across various preprocessing space limitations (across various MSM scales), our constructions achieve up to about 192× and 223× (159× and 174×) speedup versus two state-ofthe- art preprocessing parallel Pippenger algorithms on GPUs, respectively.
Francesco Aurelio Pironti, Francesco Blefari, Angelo Furfaro
The pervasive diffusion of network attached devices has given rise to technologies like Internet-of-Things (IoT) which, in turn, enabled the development of smart applications improving existing services or offering innovative ones to geographically distributed territories (e.g. smart cities). Extending similar services to regions with limited connectivity is the main challenge of smart shires. In this context, the concept of data mule has been introduced which constitutes the basis of the InDaMul framework. This framework addresses the challenge of communication in areas with sparse connection coverage leveraging Distributed Ledger Technologies (DLT). DLT is employed to incentivize owners of mobile devices to act as data mules, facilitating data transmission. This paper proposes InDaMulC2C an extension to the InDaMul framework, repurposing its infrastructure to support Client-To-Client (C2C) communications. InDaMulC2C supports direct C2C communications by allowing: (i) local offline interactions, (ii) deferral of the execution of on-chain operations subject to connection availability.
Aug 19, 2024·2024 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics
The increasing demand for renewable energy and sustainable development has led to the evolution of peer-to-peer (P2P) energy exchange models, transitioning from traditional centralized distribution to decentralized marketplaces. This paper presents an overview of the P2P energy market and then analyzes the potential of blockchain technology, highlighting how Ethereum smart contracts and oracles can provide significant support in energy trading and certification processes. By integrating data from smart meters through Ethereum oracles, we propose a basic starting theoretical framework for verifying renewable energy production, make transactions within the P2P market and use renewable energy credits within the real community. This approach provides transparency and trust among participants and addresses regulatory and technical challenges by providing a verifiable and decentralized ledger of energy transactions. Our proposal emphasizes the feasibility of a blockchain-based P2P energy trading system that empowers consumers as active participants, promoting a more resilient and sustainable energy landscape. The results of this paper are to show a theoretical blockchain architecture that facilitates renewable energy certification and decentralized trading, aligning with global sustainability goals and providing a basis for the creation of decentralized local markets and energy communities.
XinFin, in 2019, unveiled the XDC network, an enterprise-ready hybrid blockchain platform that is open-source and specializes in tokenization for real-world decentralized finance. Overseeing the XDC network is currently the XDC Foundation, a non-profit organization established to encourage the growth, enhancement, and adoption of the XDC Network through community-driven projects such as GitHub. This whitepaper discusses the real-time assessment of the XDC network's decentralization, scalability, and security aspects as well as the Nakamoto coefficient estimation that follows, which is a measure of a decentralized system's decentralization nature that quantifies the minimal number of nodes or entities needed to compromise the system. A high coefficient denotes greater decentralization, while a low number denotes increased disruption risk. The XDC network's real-time computation of the high Nakamoto coefficient demonstrates its highly decentralized character. The article also addresses the diversity of consensus and execution clients, the host distribution, the geo-distribution, and some of the outstanding issues and business considerations.
This research provides a thorough assessment of the XDC Network, a delegated proof of stake (XDPoS) consensus-based blockchain technology, across its technical, security, and business dimensions. The study evaluates the network's decentralization, scalability, and security features, including its Nakamoto coefficient, validator participation, and client distribution. Additionally, it examines the developer ecosystem, including GitHub metrics, and business aspects such as transaction costs and predictability. The findings of this research will provide valuable insights into the strengths and weaknesses of the XDC Network, informing stakeholders and decision-makers about its suitability for various use cases, particularly in trade finance, asset tokenization, and enterprise blockchain solutions.
In addressing the significant challenges caused by the expansion of data storage needs in blockchain systems, this paper explores the integration of the InterPlanetary File System (IPFS) with Substrate-based blockchain. By leveraging IPFS for off-chain storage and Substrate for on-chain operations, this system addresses the key challenges such as bloated storage, inefficiency, and accessibility while preserving data distribution and privacy. Through a comparative analysis with an Ethereum-based system, this study reveals significant advantages of the Substrate-IPFS solution. There is a significant reduction in data storage size and faster block confirmation times, leading to potentially lower transaction costs. The proposed approach enhances data privacy through the use of the Blake2 hashing algorithm. Overall, this research showcases the potential of Substrate-IPFS integration in overcoming the limitations of traditional blockchain storage approaches. Further exploration into the storage cost optimisation within the Substrate framework and additional functionalities using modular pallets could pave the way for significant advancements in the distributed data storage.
This study investigates the impact of consensus mechanism changes on cryptocurrency markets within the framework of the efficient market hypothesis, focusing on Ethereum’s transition from Proof-of-Work to Proof-of-Stake consensus, known as the Ethereum 2.0 ‘The Merge’ update. Two main hypotheses guide the enquiry: (i) ‘The Merge’ update will significantly enhance market efficiency and (ii) Ethereum’s updates will have a greater impact on market efficiency compared to other cryptocurrencies. Using the Hurst exponent’s R/S statistic, changes in Ethereum’s long-term memory characteristics before and after major hard forks are quantified. The analysis reveals substantial improvements in Ethereum’s market efficiency following the Ethereum 2.0 hard fork, attributed to the introduction of Proof-of-Stake, which enhanced transaction speed and built trust. These findings suggest a positive trajectory towards improved efficiency in Ethereum’s market, particularly with ‘The Merge’ update. In conclusion, this study contributes to understanding the role of consensus mechanisms in cryptocurrencies and provides insights into future market trends resulting from such changes.
Dinar D. Khayrutdinov, Denis A. Volkov, A.G. Mukhina
The paper focuses on a decentralized model of storing information in the educational field, a long with its advantages and disadvantages. Close attention is paid to safety, accessibility, and confidentiality, which are essential attributes of information. In addition to discussing decentralized networks from a functionality perspective, the article also describes the process of implementing distributed networks in government agencies such as universities for the purpose of persistent and secure data storage. A comparison of classic database management systems with distributed file storage systems such as IPFS has been carried out. A description of the architecture of a decentralized application is given, which allows simple direct user interaction with file management via a convenient and intuitive interface. A smart contract is considered as a link between the application and the blockchain, ensuring speed, transparency and reliability of the transaction. A decentralized application, smart contracts and an interface for information output have been developed. It is concluded that decentralized data storage is an innovation that has every chance of becoming an important component of Web3.
Decentralized Physical Infrastructure Networks (De-PINS) are secured and governed by blockchains but beyond crypto-economic incentives, they lack measures to establish trust in participating devices and their services. The verification of relevant device credentials during device registration helps to overcome this problem. However, on-chain verification in decentralized applications (dApp) discloses potentially confidential device attributes whereas off-chain verification introduces undesirable trust assumptions. In this paper, we propose a credential-based device registration (CDR) mechanism that verifies device credentials on the blockchain and leverages zero-knowledge proofs (ZKP) to protect confidential device attributes from being disclosed. We characterize CDR for DePINs, present a general system model, and technically evaluate CDR using zkSNARKs with Groth16 [1] and Marlin [2]. Our experiments give first insights into performance impacts and reveal a tradeoff between the applied proof systems.
In global grid-based cloud computing settings, performance optimization depends on effective data scheduling. The usefulness of the absolute distributed data scheduling function in controlling resource allocation, load balancing, and data dissemination across heterogeneous cloud infrastructures is assessed in this study. By taking into account variables including data locality, processing capacity, and network latency, we evaluate the function's capacity to increase system throughput while reducing scheduling overhead. Simulations that compare to current scheduling models show gains in fault tolerance, scalability, and efficiency. High-performance cloud computing is advanced by the findings, which offer insights on optimizing distributed scheduling systems. Cloud security is crucial for attracting customers and protecting data privacy. Online attackers disrupt cloud services, leading to financial growth for cloud-based organizations. Various methodologies are reviewed to develop strong security mechanisms for cloud computing, but machine learning is not enough. This research focuses on high-level technologies like Block chain and Quantum computing with Machine Learning (ML) concepts and algorithm conceptions like deep neural networks and quantum neural networks. These models reduce attacks and increase user trust, benefiting cloud service providers. The research aims to eradicate issues and promote end-to-end protection and secrecy in the cloud environment. Cloud computing is an on-demand technology that provides various services like vast computing power, unlimited storage, and on-demand web services over the internet without the need for internal infrastructure. This research focuses on data security and privacy of cloud customers using various experiments. Cyber-attacks can be Denial of Services (DoS), Distributed Denial of Services (DDoS), Man In The Middle (MITM), and malware attacks. To protect the cloud system from cyber-attacks, deep learning is used to train an intelligent honeynet system that not only protects the system from DDoS attacks but also redirects attacks towards another direction. Another approach is the Quantum Neural Network (QNN) approach, which helps identify attack patterns and categorizes them into different classes of DoS/DDoS attacks. The QNN training process addresses slowing down of the cloud system and allows valid cloud customers to access their private data in cloud storage. Another approach is Zero Knowledge Proof (ZKP) technology, which verifies the authenticity of cloud users by polarizing photons at a specific angle. This verifier model allows cloud customers to access sensitive data and only cloud services provided by the cloud service provider. Blockchain, a powerful security framework, is used to address increasing security vulnerabilities. The Quantum-Blockchain framework incorporates the quantum superimposition principle to prevent data tampering, ensuring data privacy and data security. This research aims to address intrusion detection and data storage security challenges in the cloud computing environment using collaborative efforts from Machine Learning and advanced technologies like Quantum Computing and Blockchain. The cloud manifesto and security alliance need to be standardized to ensure privacy and security. Current research is limited due to lack of security and privacy standards between cloud vendors and users. Future studies should focus on advanced technologies like hybrid cloud, artificial intelligence, quantum computing, data mining, machine learning, big data, and cryptography to enhance security and prevent cyber-attacks.
Smart contracts led to the emergence of the decentralized finance (DeFi) marketplace within blockchain ecosystems, where diverse participants engage in financial activities. In traditional finance, there are possibilities to create values, e.g., arbitrage offers to create value from market inefficiencies or front-running offers to extract value for the participants having privileged roles. Such opportunities are readily available -- searching programmatically in DeFi. It is commonly known as Maximal Extractable Value (MEV) in the literature. In this survey, first, we show how lucrative such opportunities can be. Next, we discuss how protocol-following participants trying to capture such opportunities threaten to sabotage blockchain's performance and the core tenets of decentralization, transparency, and trustlessness that blockchains are based on. Then, we explain different attempts by the community in the past to address these issues and the problems introduced by these solutions. Finally, we review the current state of research trying to restore trustlessness and decentralization to provide all DeFi participants with a fair marketplace.