This article examines the transformative advancements in real-time payment processing optimization and their profound impact on digital commerce. It explores how millisecond-level processing improvements significantly enhance conversion rates in high-volume e-commerce environments. Key innovations discussed include intelligent predictive routing algorithms that leverage historical data to make real-time transaction routing decisions, the implementation of standardized interfaces like Payment Request API and ISO 20022, and privacy-preserving optimization techniques compliant with evolving regulatory frameworks. It further analyzes the substantial impact of these optimizations on e-commerce conversion rates, demonstrating how they reduce cart abandonment and improve customer trust and retention. Looking ahead, the article considers emerging technologies such as Central Bank Digital Currencies and distributed ledger systems that promise to further revolutionize payment processing with faster settlement times, lower costs, and expanded financial inclusion. The article findings suggest that payment processing optimization has evolved beyond technical consideration to become a strategic business imperative with measurable revenue impact and broader economic implications.
Atefeh Zareh Chahoki, Maurice Herlihy, Marco Roveri
Conthereum is a concurrent Ethereum solution for intra-block parallel transaction execution, enabling validators to utilize multi-core infrastructure and transform the sequential execution model of Ethereum into a parallel one. This shift significantly increases throughput and transactions per second (TPS), while ensuring conflict-free execution in both proposer and attestor modes and preserving execution order consistency in the attestor. At the heart of Conthereum is a novel, lightweight, high-performance scheduler inspired by the Flexible Job Shop Scheduling Problem (FJSS). We propose a custom greedy heuristic algorithm, along with its efficient implementation, that solves this formulation effectively and decisively outperforms existing scheduling methods in finding suboptimal solutions that satisfy the constraints, achieve minimal makespan, and maximize speedup in parallel execution. Additionally, Conthereum includes an offline phase that equips its real-time scheduler with a conflict analysis repository obtained through static analysis of smart contracts, identifying potentially conflicting functions using a pessimistic approach. Building on this novel scheduler and extensive conflict data, Conthereum outperforms existing concurrent intra-block solutions. Empirical evaluations show near-linear throughput gains with increasing computational power on standard 8-core machines. Although scalability deviates from linear with higher core counts and increased transaction conflicts, Conthereum still significantly improves upon the current sequential execution model and outperforms existing concurrent solutions under a wide range of conditions.
Modular arithmetic, particularly modular reduction, is widely used in cryptographic applications such as homomorphic encryption (HE) and zero-knowledge proofs (ZKP). High-bit-width operations are crucial for enhancing security; however, they are computationally intensive due to the large number of modular operations required. The lookup-table-based (LUT-based) approach, a ``space-for-time'' technique, reduces computational load by segmenting the input number into smaller bit groups, pre-computing modular reduction results for each segment, and storing these results in LUTs. While effective, this method incurs significant hardware overhead due to extensive LUT usage. In this paper, we introduce ALLMod, a novel approach that improves the area efficiency of LUT-based large-number modular reduction by employing hybrid workloads. Inspired by the iterative method, ALLMod splits the bit groups into two distinct workloads, achieving lower area costs without compromising throughput. We first develop a template to facilitate workload splitting and ensure balanced distribution. Then, we conduct design space exploration to evaluate the optimal timing for fusing workload results, enabling us to identify the most efficient design under specific constraints. Extensive evaluations show that ALLMod achieves up to $1.65\times$ and $3\times$ improvements in area efficiency over conventional LUT-based methods for bit-widths of $128$ and $8,192$, respectively.
Popular technologies such as blockchain and zero-knowledge proof, which have already entered the enterprise space, heavily use cryptography as the core of their protocol stack. One of the most used systems in this regard is Elliptic Curve Cryptography, precisely the point multiplication operation, which provides the security assumption for all applications that use this system. As this operation is computationally intensive, one solution is to offload it to specialized accelerators to provide better throughput and increased efficiency. In this paper, we explore the use of Field Programmable Gate Arrays (FPGAs) and the High-Level Synthesis framework of AMD Vitis in designing an elliptic curve point arithmetic unit (point adder) for the secp256k1 curve. We show how task-level parallel programming and data streaming are used in designing a RISC processor-like architecture to provide pipeline parallelism and increase the throughput of the point adder unit. We also show how to efficiently use the proposed processor architecture by designing a point multiplication scheduler capable of scheduling multiple batches of elliptic curve points to utilize the point adder unit efficiently. Finally, we evaluate our design on an AMD-Xilinx Alveo-family FPGA and show that our point arithmetic processor has better throughput and frequency than related work.
Multi-scalar multiplication (MSM) is the primary building block in many pairing-based zero-knowledge proof (ZKP) systems. MSM at large scales has become the main bottleneck in ZKP implementations. Inspired by existing SIMD-accelerated work, we are focused on accelerating MSM computing efficiency using SIMD instructions in a single CPU environment. First, we propose a SIMD-accelerated MSM computing architecture with no write conflicts and constant memory overheads. This architecture utilizes multithreading to achieve task-level and loop-level parallelism and employs a three-tier buffer mechanism to maximize the utilization of the SIMD engine. Instanced with AVX512-IFMA instructions, we implement six SIMD elliptic curve arithmetic engines for different point addition in three coordinate systems and two groups. Moreover, we integrate our AVX-MSM implementation into the libsnark library, naming it AVX-ZK. In more detail, point deduplication and “Three-Stage” memory optimization are proposed to address problems existing in practical applications. Based on the RELIC library, our performance results on the BLS12-381 curve show that our AVX-MSM achieves up to 27.86x speedup over the most popular Pippenger algorithm. Compared with libsnark, our AVX-ZK implementation achieves over 11.53x (up to 20.26x) speedup under standard benchmarks.
With the widespread adoption of Zero-Knowledge Proof systems, particularly ZK-SNARK, the efficiency of proof generation, encompassing both the witness generation and proof computation phases, has become a significant concern. While substantial efforts have successfully accelerated proof computation, progress in optimizing witness generation remains limited, which inevitably hampers overall efficiency. In this paper, we propose Yoimiya, a scalable framework with pipeline, to optimize the efficiency in ZK-SNARK systems. First, Yoimiya introduces an automatic circuit partitioning algorithm that divides large circuits of ZK-SNARK into smaller subcircuits, the minimal computing units with smaller memory requirement, allowing parallel processing on multiple units. Second, Yoimiya decouples witness generation from proof computation, and achieves simultaneous executions over units from multiple circuits. Moreover, Yoimiya enables each phase scalable separately by configuring the resource distribution to make the time costs of the two phases aligned, maximizing the resource utilization. Experimental results confirmed that our framework effectively improves the resource utilization and proof generation speed.
In modern distributed systems, achieving consensus and reconciliation among diverse nodes across varying network conditions is a significant challenge. CohortSync, a novel micro-cohort-based protocol, addresses this challenge by leveraging scalable and fault-tolerant mechanisms to ensure data consistency and system reliability. The core innovation of CohortSync lies in its utilization of dynamically formed micro-cohorts, which are small, manageable groups of nodes that collaborate to achieve consensus without the overhead associated with traditional large-scale consensus protocols. CohortSync operates by first classifying nodes based on their network latency, data relevance, and operational load. This classification enables the protocol to intelligently form micro-cohorts that are geographically and contextually optimized, reducing the latency typically experienced in global consensus operations. Each micro-cohort is responsible for a subset of the reconciliation tasks, allowing for parallel processing and significantly reducing the time to reach consensus. The protocol incorporates a hybrid approach to consensus that combines elements of both deterministic and probabilistic consensus mechanisms. This hybrid model allows CohortSync to maintain high availability and consistency, even in the face of node failures or network partitions. By adapting the consensus mechanism based on real-time network performance and node responsiveness, CohortSync can dynamically adjust its operations to maintain system performance and data accuracy. Another key feature of CohortSync is its reconciliation process, which uses a version-controlled state reconciliation algorithm. This algorithm ensures that all nodes within a micro-cohort maintain a synchronized state, with conflicts resolved through a majority rule among the cohort members. This approach not only minimizes the risk of data divergence but also optimizes the reconciliation process to be both time-efficient and resource-conservative. CohortSync also integrates a continuous learning component that analyzes past consensus rounds to optimize future cohort formation and consensus strategies. This machine learning-driven adaptability makes the protocol robust against evolving network conditions and varying operational loads across nodes. The protocol has been tested in various simulated environments that mimic real-world distributed systems across different industries, including finance, healthcare, and e-commerce. The results demonstrate that CohortSync significantly outperforms existing consensus protocols in terms of scalability, fault tolerance, and operational efficiency. In conclusion, CohortSync presents a transformative approach to consensus and reconciliation in distributed systems. By decentralizing the consensus process into manageable micro-cohorts and integrating adaptive learning mechanisms, CohortSync offers a scalable, efficient, and robust solution that can meet the demands of contemporary distributed computing environments.
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.
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.
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.
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.
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.
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.
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.
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.
This paper presents, for the first time, the Mediterraneous protocol. It is designed to support the development of an Internet of digital services, owned by their creators, and consumed by users by presenting their decentralised digital identity and a proof of service purchase. Mediterraneous is Self-Sovereign Identity (SSI) native, integrating the SSI model at the core of its working principles to overcome the limitations resulting from using pseudonyms and centralised access control of existing Web3 solutions.