Cloud storage is crucial for managing large datasets, but dependence on a single cloud space raises security concerns. Conversely, Distributed Ledger Technology (DLT) provides a secure cloud-based storage system operating on a peer-to-peer network. In our proposed system, user files are encrypted and distributed across multiple peers using the Interplanetary File System (IPFS) protocol, with hash values stored in the DLT. This project integrates DLT and a cloud platform for storing files from various users. Our project, Distributed Cloud File Sharing Technology (DCFST), merges distributed computing principles with cloud technology to overcome traditional file-sharing limitations. It enables secure file sharing, storage, and access across interconnected devices, ensuring data integrity and authentication through blockchain. Smart contracts automate access control, granting precise content control to users. DCFST orchestrates a peer-to-peer network of nodes, with a central coordinator managing node discovery, file indexing, and load balancing. The implementation utilizes Python, J avaScrlpt, and Ethereum for smart contract execution. DCFST introduces a resilient, user-centric file-sharing ecosystem by merging distributed systems with cloud infrastructure. It contributes to decentralized technology evolution, revolutionizing data collaboration and access paradigms.
Yuan Huang, Rong Wang, Xiangping Chen, Zibin Zheng
An increasing number of investors are active on Ethereum, resulting in numerous transactions. These historical transactions can be applied to complete contract testing. For example, it can be used for gas optimization or contract repair to verify that improved contracts meet expectations. Most existing methods deploy private chains to use non-real transactions for contract verification instead of actual historical transactions on the Ethereum mainnet. The challenge of using actual historical transactions for verification is that Ethereum only records the latest state of the account and cannot restore the execution of historical transactions. Due to contract code changes in contract gas optimization, contract defect repair, and other scenarios, we need to test the execution of the contract code before and after the change. However, existing tools cannot customize and modify historical transactions for testing purposes. Therefore, we propose an efficient transaction replay platform,EthReplayer, which can not only replay the historical transactions of Ethereum quickly and faithfully but also realize the modification of transactions to achieve the purpose of testing with actual transactions. Experimental results show that our replay speed is 1.5 times the fastest available, and it only takes 29,594 seconds to replay 1,200 million blocks. In addition, it is applied to contract repair verification, gas optimization verification, and gas estimation, and the results prove the effectiveness ofEthReplayer.
Number theoretic transform (NTT) is a fundamental building block in emerging cryptographic constructions such as fully homomorphic encryption (FHE), post-quantum cryptography (PQC), and zero-knowledge proof (ZKP). In this work, we introduce Proteus, an open-source parametric hardware to generate pipelined architectures for the NTT. For a given parameter set including the polynomial degree and size of the coefficient modulus, Proteus can generate Radix-2 NTT architectures using single-path delay feedback (SDF) and multipath delay commutator (MDC) approaches. We also present a detailed analysis of NTT implementation approaches and use several optimizations to achieve the best NTT configuration. Our evaluations demonstrate performance gain up to$1.8\times$compared to SDF and MDC-based NTT implementations in the literature. Our SDF and MDC architectures use$1.75\times$and$6.5\times$less DSPs, and$3\times$and$10.5\times$less BRAMs, respectively, compared to state-of-the-art SDF and MDC-based NTT implementations.
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.
Abstract Blockchain infrastructures have emerged as a disruptive technology and have led to the realization of cryptocurrencies (peerâtoâpeer payment systems) and smart contracts. They can have a wide range of application areas in eâScience due to their open, public nature and global accessability in a trustless manner. We propose and implement a smart contract called eBlocBroker, which is an autonomous blockchainâbased middleware system for volunteer computing and providing data resources for eâScience. The eBlocBroker infrastructure connects requesters who need to combine applications (jobs) with datasets and run them via an Ethereumâbased private blockchain network (Bloxberg) on providers that utilize computational and data resources on clouds or home servers. It uses cloud storage, such as B2DROP, IPFS, or Google Drive, to store and transfer data between requesters and providers. Each provider utilizes the Slurm workload manager to execute jobs submitted through eBlocBroker. In this paper, we demonstrate how an autonomous organization programmed as a smart contract can be used to deploy a marketplace that supports data and computationâintensive research projects. We propose a cost model implemented as a function in the smart contract which calculates and records computation and dataset usage costs. We develop a Pythonâbased system to communicate with eBlocBroker and orchestrate jobs' execution on the provider's end. We present eBlocBroker's features, infrastructure, implementation, algorithms and experimental results.
Yutian Chen, Cong Peng, Yu Dai, Min Luo · 5 authors
Multi-scalar multiplication (MSM) is an important building block in most of elliptic-curve-based zero-knowledge proof systems, such as Groth16 and PLONK. Recently, Lu et al. proposed cuZK, a new parallel MSM algorithm on GPUs. In this paper, we revisit this scheme and present a new GPU-based implementation to further improve the performance of MSM algorithm. First, we propose a novel method for mapping scalars into Pippengerâs bucket indices, largely reducing the number of buckets compared to the original Pippenger algorithm. Second, in the case that memory is sufficient, we develop a new efficient algorithm based on homogeneous coordinates in the bucket accumulation phase. Moreover, our accumulation phase is load-balanced, which means the parallel speedup ratio is almost linear growth as the number of device threads increases. Finally, we also propose a parallel layered reduction algorithm for the bucket aggregation phase, whose time complexity remains at the logarithmic level of the number of buckets. The implementation results over the BLS12-381 curve on the V100 graphics card show that our proposed algorithm achieves up to 1.998x, 1.821x and 1.818x speedup compared to cuZK at scales of 221, 222, and 223, respectively.
Blockchain technology is not just about digital currencies but even more about group coordination via software. Decentralized Autonomous Organizations (DAOs) are a broad organizational form for decentralized, blockchain-based coordination towards shared objectives. The concept of DAOs is driven by an ideology of self-governance through technological infrastructure. Drawing on the Science & Technology Studies conception of âvulnerability,â this chapter offers an analytical approach to identifying DAO vulnerabilities across multiple dimensions termed âDAO Vulnerability Mapping.â Vulnerability mapping is not only for analytical purposes but also to identify, prioritize, and address vulnerabilities towards improving resilience. DAO vulnerabilities can occur along social, technical, legal, economic, and environmental dimensions, and can emerge from inside a DAO (endogenously), or from outside a DAO (exogenously). This approach is pertinent in socio-technical contexts, such as blockchain-based organization, where social and technical dynamics are inextricably linked. In this chapter, these multidisciplinary dimensions of DAO vulnerabilities are explored in practice via a qualitative case study of a âliquid stakingâ DAO called Lido DAO. DAO vulnerability mapping is both a conceptual analytical tool and a methodological approach for studying and understanding digitally mediated organizations, as well as the novel practices of those engaging in Decentralized Autonomous Organizations.
Scalability in blockchain remains a significant challenge, especially when prioritizing decentralization and security. The Ethereum community has proposed comprehensive data-sharding techniques to overcome storage, computational, and network processing limitations. In this context, the propagation and availability of large blocks become the subject of research to achieve scalable data-sharding. This paper provides insights after exploring the usage of a Kademlia-based Distributed Hash Table (DHT) to enable Data Availability Sampling (DAS) in Ethereum. It presents a DAS-DHT simulator to study this problem and validates the results of the simulator with experiments in a real DHT network, InterPlanetary File System (IPFS). Our results help us understand what parts of DAS can be achieved based on existing Kademlia DHT solutions and which ones cannot. We discuss the limitations of DHT solutions and discuss other alternatives.
Abstract Scientific workflows are essential for many applications, enabling the configuration and execution of complex tasks across distributed resources. In this paper, we contribute an Ethereum blockchain-based scientific workflow execution manager, which distributes workflows to run on cluster computing providers that utilize the Slurm workload manager to execute them. We extended our blockchain-based autonomous resource broker called eBlocBroker, which is a DAO-based decentralized coordinator, by providing distributed workflow execution via blockchain. Through various tests, we demonstrate how our eBlockBroker autonomous organization, which is programmed as a smart contract, can manage scientific workflow submission, scheduling, and execution on cluster computing providers. The utilization of blockchain for distributed workflow execution is a new concept. We are motivated because our system has been developed with e-Science in mind where scientific workflows are widely utilized.
Principal Engineer, Discover Financial Services, Houston, TX, USA, AdisheshuReddy Kommera
The Event-Driven Data Mesh Integration pattern revolutionizes modern data sharing by blending eventdriven architecture and the data mesh paradigm. This innovative approach decentralizes data ownership, enabling organizational domains to manage their data autonomously while ensuring real-time responsiveness and seamless scalability. Central to the pattern are event broker layers, domain-oriented data producers, event enrichment nodes, and a self-serve data product catalog. Key features include schema validation, policy-based governance, and real-time enrichment, fostering efficiency, compliance, and agility. Integrating AI-powered self-healing mechanisms further enhances resilience, automates recovery processes, and optimizes resource allocation. Applications span various sectors, from operational systems to analytics pipelines, enabling real-time decision-making and continuous improvement. This approach empowers organizations to innovate faster while maintaining robust data governance, scalability, and interoperability across autonomous domains, paving the way for intelligent and dynamic data ecosystems.
Todayâs organizations are seeking to develop new solutions to enhance the security, transparency, and accessibility of their cloud-based infrastructure. Integrating distributed ledger technology with cloud-based infrastructure can bring significant advantages to organizations but involves a complex process that requires meticulous planning, design, and execution. The process commences with choosing the suitable DLT platform, and one must possess a thorough understanding of distributed ledger technology and cloud computing technologies, including their unique features, capabilities, and compromises.
Distributed Outsourced Storage systems, exemplified by the InterPlanetary File System (IPFS), offer compelling alternatives to traditional centralized cloud storage by emphasizing resilience and openness. Advancing this paradigm, Decentralized Storage (DS) markets leverage distributed ledgers to facilitate the monetization of outsourced storage. However, these markets often prioritize security over cost-efficiency, leading to high costs in existing DS markets. In our work, we introduce a middleware service, DWare, utilizing trusted hardware to balance security and cost efficiency. DWare offers two key advantages: 1) It enhances storage auditing efficiency by delegating computational tasks and standardizing the batched audit process. This approach offers a more feasible solution for validating outsourced storage with recurring pay-offs. 2) It implements secure and verifiable data deduplication, thereby increasing storage efficiency and reducing operational costs. This step, commonplace in cloud storage services, remains largely unexplored in current DS designs. While DWare could empirically reduce costs to levels near raw storage fees, it entails certain security concessions due to middleware involvement. To address this, we propose a hybrid trust security model, granting data owners the flexibility to adjust the security-cost balance as needed.
Traditional banking systems act as intermediaries, assessing risks and profiting from interest rate differentials. Credit scores, provided by trusted bureaus, are commonly used to evaluate the creditworthiness of borrowers. Cryptocurrencies have emerged as a significant and innovative medium due to their decentralized nature, operating without reliance on a central authority, such as a government.This report describes a project to implement the Autonomous Lending system on the Ethereum Platform (ALOE), as proposed in [1], aiming to seamlessly integrate traditional credit scoring methodologies for evaluating a borrower's risk of default. The objective of this project report is to establish a robust understanding of cryptocurrencies and the Ethereum platform and describe the implementation of pivotal components of the ALOE system, as presented by Austin, Potika, and Pollett in 2023. Specifically, the report aims to incorporate essential functionalities of the Credit Bureau Smart Contract (CBSC). This entails the creation of a Notary tasked with verifying borrowers using real-world FICO scores, SSNs, and Ethereum Addresses. The Notary further divides the user's identityamong various auditors and invokes the initializeLedger function to establish a credit score for the borrower. The CBSC plays a pivotal role in connecting lenders and borrowers. Finally, in cases of loan repayment failure, lenders have the option to engage auditors to disclose the client's identity.
Fernando Bereta dos Reis, Mark Borkum, Monish Mukherjee, Hayden Reeve · 5 authors
This report explores the potential of distributed ledger technology (DLT) as a transformative tool to enhance fault-tolerant operations in electrical distribution systems. Leveraging DLT's core attributes, including an immutable decentralized ledger, distributed consensus mechanisms, and state replication capabilities, this study focuses on three critical use cases. A central aspect of this research centers on the utilization of a consensus-driven ledger, providing actors within the system, such as distributed resources, with access to a reliable data repository. This empowers these actors to collaborate effectively and make informed decisions, all securely recorded on the blockchain. The first use case concentrates on data configuration, utilizing mathematical criteria---particularly, the chi-squared test for gross error detection---to identify trustworthy sensors for advanced decision-making. Building upon this foundation of trust, the second use case, topology identification, accurately determines circuit breaker states, unveiling the distribution network's topology. Ultimately, the third use case leverages this trust to execute switching actions, reconfiguring feeders and restoring power to disconnected customers after fault events. The concept of trust serves as a cornerstone in this approach, marking a departure from traditional fault location, isolation, and service restoration (FLISR) methods. Additionally, the blockchain-based architecture introduces decentralization, empowering disconnected areas to make autonomous decisions, even when communication with a central control center is disrupted. The primary contributions of this report are twofold: (1) a novel approach for evaluating distribution system voltage areas while preserving data ownership and (2) the implementation of interactions between distribution network areas using the actor model. Unlike the previous sequential approach for evaluating the area connection voltages, which required a radial network topology, this study's area model reduction enables a more versatile approach. The area model reduction addresses issues of prolonged data waiting times and multiple points of failure within the previous approach. Notably, the presented evaluation for the reduced network model area connection reveals a significant increase in the differences in voltage magnitudes. Simulation and evaluation of area agents across four distinct cases elucidate the area-level interaction behavior during a fault event. Simulations demonstrate that the proposed distributed FLISR (DFLISR) approach can successfully restore service to an affected area. Varying message delays and message loss probabilities in each simulation case underscore their impacts on restoration times, ranging from 3 min and 32 s to 6 min and 19 s. In contrast, power is not restored in an area in one of our simulation cases.
Conor Flynn, Kristin P. Bennett, John Erickson, Aaron Green · 5 authors
With the agile development process of most academic and corporate entities, designing a robust computational back-end system that can support their ever-changing data needs is a constantly evolving challenge. We propose the implementation of a data and language-agnostic system design that handles different data schemes and sources while subsequently providing researchers and developers a way to connect to it that is supported by a vast majority of programming languages. To validate the efficacy of a system with this proposed architecture, we integrate various data sources throughout the decentralized finance (DeFi) space, specifically from DeFi lending protocols, retrieving tens of millions of data points to perform analytics through this system. We then access and process the retrieved data through several different programming languages (R-Lang, Python, and Java). Finally, we analyze the performance of the proposed architecture in relation to other high-performance systems and explore how this system performs under a high computational load.
In the rapidly evolving domain of distributed ledger technology, scalability and interoperability have become paramount challenges for both academic and industry sectors. In this paper, we introduce a comprehensive formal model to address atomic composability across multiple rollups on Ethereum. The proposed model incorporates mechanisms like buffering, dependency management, concurrency control, and the groundbreaking zero-knowledge proofs. Moreover, we evaluate its practical repercussions, strengths, and weaknesses, ensuring resilience against manipulative or erroneous actions. The application of the proposed model to shared sequencers and other existing solutions accentuates its versatility and universality.
Fragmented energy data silos pose a significant obstacle to efficient data management and collaboration in the European energy sector. Creating a unified European energy dataspace aligned with GAIA-X principles (transparency, data security, data protection, interoperability and scalability) is crucial to overcoming this challenge. This paper presents a solution that combines a Distributed Ledger Technology (DLT)-based data marketplace with the International Data Spaces (IDS) Architecture. By utilizing DLT, the proposed marketplace enables secure and transparent tracking of energy datasets within the dataspace. Integration with the IDSA ensures compliance with GAIA-X, promoting interoperability and seamless data exchange. It enables the consolidation of diverse data sources into a unified dataspace, eliminating silos. This fosters efficient and secure data exchange, promotes transparency and trust, and supports collaboration and innovation in the energy sector.
The InterPlanetary File System (IPFS) is currently the largest decentralized storage solution in operation, with thousands of active participants and millions of daily content transfers. IPFS is used as remote data storage for numerous blockchain-based smart contracts, Non-Fungible Tokens (NFT), and decentralized applications. We present a content censorship attack that can be executed with minimal effort and cost, and that prevents the retrieval of any chosen content in the IPFS network. The attack exploits a conceptual issue in a core component of IPFS, the Kademlia Distributed Hash Table (DHT), which is used to resolve content IDs to peer addresses. We provide efficient detection and mitigation mechanisms for this vulnerability. Our mechanisms achieve a 99.6\% detection rate and mitigate 100\% of the detected attacks with minimal signaling and computational overhead. We followed responsible disclosure procedures, and our countermeasures are scheduled for deployment in the future versions of IPFS.
Non-fungible tokens (NFTs) are becoming increasingly popular in Play-to-Earn (P2E) Web3 applications as a means of incentivizing user engagement. In Web3, users with NFTs ownership are entitled to monetize them. However, due to lack of objective NFT valuation, which makes NFT value determination challenging, P2E applications ecosystems have experienced inflation. In this paper, we propose a method that enables NFT inflation value management in P2E applications. Our method leverages the contribution-rewards model proposed by Curve Finance and the automated market maker (AMM) of decentralized exchanges. In decentralized systems, P2E Web3 applications inclusive, not all participants contribute in good faith. Therefore, rewards are provided to incentivize contribution. Our mechanism proves that burning NFTs, indicating the permanent removal of NFTs, contributes to managing inflation by reducing the number of NFTs in circulation. As a reward for this contribution, our method mints a compensation (CP) token as an ERC-20 token, which can be exchanged for NFTs once enough tokens have been accumulated. To further increase the value of the CP token, we suggest using governance tokens and CP tokens to create liquidity pools for AMM. The value of the governance token is determined by the market, and the CP token derives its value from the governance token in AMM. The CP token can determine its worth based on the market value of the governance token. Additionally, since CP tokens are used for exchanging NFTs, the value of the NFT is ultimately determined by the value of the CP token. To further illustrate our concept, we show how to adjust burning rewards based on factors such as the probability of upgrading NFTs' rarity or the current swap ratio of governance and CP tokens in AMM.
Since its inception as one of the Bitcoin's underpinning technologies, the concept of Blockchain has traversed a long way from being merely a secure distributed ledger meant only for storing cryptocurrencies-based financial transactions to implementing innovative and revolutionary distributed systems for multifarious purposes.One of the contemporary and out-of-the-box Blockchain-based projects, namely Hyperledger, promises to make preeminent use of this technology by promoting cross-industry collaboration in developing Blockchain-based solutions, thus, opening a new chapter in distributed computing.However, attributing to its underlying design, leveraging Hyperledger-Fabric (HF) features still lacks an analysis from the perspective of security risks and efficiency concerns pertaining to real-time distributed computing-based systems and applications.In this regard, we investigate the HF architecture, along with various research endeavours undergone by researchers in recent years to combat its security and performance-related challenges.In light of the identified limitations and bottlenecks, we present our conceptual proposal and feasible insights for improving the efficacy of HF-based systems while not compromising their security.
Tobias Adrian, Federico Grinberg, Tommaso Mancini Griffoli, Robert M. Townsend · 5 authors
Cross-border payments can be slow, expensive, and risky. They are intermediated by counterparties in different jurisdictions which rely on costly trusted relationships to offset the lack of a common settlement asset as well as common rules and governance. In this paper, we present a vision for a multilateral platform that could improve cross-border payments, as well as related foreign exchange transactions, risk sharing, and more generally, financial contracting. The approach is to leverage technological innovations for public policy objectives. A common ledger, smart contracts, and encryption offer significant gains to market efficiency, completeness, and access, as well as to transparency, transaction and compliance costs, and safety. This paper is a first step aiming to stimulate further work in this space.
In order to realize collaboration on a global scale, academic research requires often large quantities of data to be shared between geographically dispersed organizations. The requirement to protect and govern data in a network of loosely coupled, autonomous institutions is an incentive for decentralized solutions, where the participants are in full control of their data without trusting a third-party provider to store and process the data. In order to increase data availability and fault tolerance in decentralized collaborative systems, we propose a layer, which is based on replication and decentralized authority over the data. The solution consists of an idea of peer-sets, which are groups of peers implementing collective data management, a consensus protocol which synchronizes a distributed ledger between peers, and an atomic commitment protocol used to implement optional two-way references between documents. This architecture may be utilized in various decentralized collaborative data-sharing systems, such as Onedata.