arXiv subjects

Guangsheng Yu

Publications and source records attributed to Guangsheng Yu.

At least 55 records · Page 3Linked to original sources

arXiv2024

TBDD: A New Trust-based, DRL-driven Framework for Blockchain Sharding in IoT

Integrating sharded blockchain with IoT presents a solution for trust issues and optimized data flow. Sharding boosts blockchain scalability by dividing its nodes into parallel shards, yet it's vulnerable to the $1\%$ attacks where dishonest nodes target a shard to corrupt the entire blockchain. Balancing security with scalability is pivotal for such systems. Deep Reinforcement Learning (DRL) adeptly handles dynamic, complex systems and multi-dimensional optimization. This paper introduces a Trust-based and DRL-driven (\textsc{TbDd}) framework, crafted to counter shard collusion risks and dynamically adjust node allocation, enhancing throughput while maintaining network security. With a comprehensive trust evaluation mechanism, \textsc{TbDd} discerns node types and performs targeted resharding against potential threats. The model maximizes tolerance for dishonest nodes, optimizes node movement frequency, ensures even node distribution in shards, and balances sharding risks. Rigorous evaluations prove \textsc{TbDd}'s superiority over conventional random-, community-, and trust-based sharding methods in shard risk equilibrium and reducing cross-shard transactions.

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arXiv2023

Towards Web3 Applications: Easing the Access and Transition

Web3 is leading a wave of the next generation of web services that even many Web2 applications are keen to ride. However, the lack of Web3 background for Web2 developers hinders easy and effective access and transition. On the other hand, Web3 applications desire for encouragement and advertisement from conventional Web2 companies and projects due to their low market shares. In this paper, we propose a seamless transition framework that transits Web2 to Web3, named WebttCom, after exploring the connotation of Web3 and the key differences between Web2 and Web3 applications. We also provide a full-stack implementation as a use case to support the proposed framework, followed by interviews with five participants that show four positive and one natural response. We confirm that the proposed framework WebttCom addresses the defined research question, and the implementation well satisfies the framework WebttCom in terms of strong necessity, usability, and completeness based on the interview results.

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arXiv2023

Cryptocurrency in the Aftermath: Unveiling the Impact of the SVB Collapse

In this paper, we explore the aftermath of the Silicon Valley Bank (SVB) collapse, with a particular focus on its impact on crypto markets. We conduct a multi-dimensional investigation, which includes a factual summary, analysis of user sentiment, and examination of market performance. Based on such efforts, we uncover a somewhat counterintuitive finding: the SVB collapse did not lead to the destruction of cryptocurrencies; instead, they displayed resilience.

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arXiv2023

BRC-20: Hope or Hype

BRC-20 (short for Bitcoin Request for Comment 20) token mania was a key storyline in the middle of 2023. Setting it apart from conventional ERC-20 token standards on Ethereum, BRC-20 introduces non-fungibility to Bitcoin through an editable field in each satoshi (0.00000001 Bitcoin, the smallest unit), making them unique. In this paper, we pioneer the exploration of this concept, covering its intricate mechanisms, features, and state-of-the-art applications. By analyzing the multi-dimensional data spanning over months with factual investigations, we conservatively comment that while BRC-20 expands Bitcoin's functionality and applicability, it may still not match Ethereum's abundance of decentralized applications and similar ecosystems.

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arXiv2023

Dataset Obfuscation: Its Applications to and Impacts on Edge Machine Learning

Obfuscating a dataset by adding random noises to protect the privacy of sensitive samples in the training dataset is crucial to prevent data leakage to untrusted parties for edge applications. We conduct comprehensive experiments to investigate how the dataset obfuscation can affect the resultant model weights - in terms of the model accuracy, Frobenius-norm (F-norm)-based model distance, and level of data privacy - and discuss the potential applications with the proposed Privacy, Utility, and Distinguishability (PUD)-triangle diagram to visualize the requirement preferences. Our experiments are based on the popular MNIST and CIFAR-10 datasets under both independent and identically distributed (IID) and non-IID settings. Significant results include a trade-off between the model accuracy and privacy level and a trade-off between the model difference and privacy level. The results indicate broad application prospects for training outsourcing in edge computing and guarding against attacks in Federated Learning among edge devices.

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arXiv2023

Cross-chain between a Parent Chain and Multiple Side Chains

In certain Blockchain systems, multiple Blockchains are required to operate cooperatively for security, performance, and capacity considerations. This invention defines a cross-chain mechanism where a main Blockchain issues the tokens, which can then be transferred and used in multiple side Blockchains to drive their operations. A set of witnesses are created to securely manage the token exchange across the main chain and multiple side chains. The system decouples the consensus algorithms between the main chain and side chains. We also discuss the coexistence of the main tokens and the native tokens in the side chains.

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arXiv2023

A Secure Aggregation for Federated Learning on Long-Tailed Data

As a distributed learning, Federated Learning (FL) faces two challenges: the unbalanced distribution of training data among participants, and the model attack by Byzantine nodes. In this paper, we consider the long-tailed distribution with the presence of Byzantine nodes in the FL scenario. A novel two-layer aggregation method is proposed for the rejection of malicious models and the advisable selection of valuable models containing tail class data information. We introduce the concept of think tank to leverage the wisdom of all participants. Preliminary experiments validate that the think tank can make effective model selections for global aggregation.

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arXiv2023

Distributed Trust Through the Lens of Software Architecture

Distributed trust is a nebulous concept that has evolved from different perspectives in recent years. While one can attribute its current prominence to blockchain and cryptocurrency, the distributed trust concept has been cultivating progress in federated learning, trustworthy and responsible AI in an ecosystem setting, data sharing, privacy issues across organizational boundaries, and zero trust cybersecurity. This paper will survey the concept of distributed trust in multiple disciplines. It will take a system/software architecture point of view to look at trust redistribution/shift and the associated tradeoffs in systems and applications enabled by distributed trust technologies.

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arXiv2023

An Empirical Study on Snapshot DAOs

Decentralized Autonomous Organization (DAO) is an organization constructed by automatically executed rules such as via smart contracts, holding features of the permissionless committee, transparent proposals, and fair contribution by stakeholders. As of Nov 2022, DAO has impacted over \$11.2B market caps. However, there are no substantial studies focused on this emerging field. To fill the gap, we start from the ground truth by empirically studying the breadth and depth of the DAO markets in mainstream public chain ecosystems in this paper. We dive into the most widely adoptable DAO launchpad, \textit{Snapshot}, which covers 95\% in the wild DAO projects for data collection and analysis. By integrating extensive enrolled DAOs and corresponding data measurements, we explore statistical data from Snapshot and try to demystify its undiscovered truths by delivering a series of summarised insights. We also present DAO status, patterns, distribution, and trends. To our knowledge, this is the first empirical study putting concentration on DAO spaces.

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arXiv2023

Blockchained Federated Learning for Internet of Things: A Comprehensive Survey

The demand for intelligent industries and smart services based on big data is rising rapidly with the increasing digitization and intelligence of the modern world. This survey comprehensively reviews Blockchained Federated Learning (BlockFL) that joins the benefits of both Blockchain and Federated Learning to provide a secure and efficient solution for the demand. We compare the existing BlockFL models in four Internet-of-Things (IoT) application scenarios: Personal IoT (PIoT), Industrial IoT (IIoT), Internet of Vehicles (IoV), and Internet of Health Things (IoHT), with a focus on security and privacy, trust and reliability, efficiency, and data heterogeneity. Our analysis shows that the features of decentralization and transparency make BlockFL a secure and effective solution for distributed model training, while the overhead and compatibility still need further study. It also reveals the unique challenges of each domain presents unique challenges, e.g., the requirement of accommodating dynamic environments in IoV and the high demands of identity and permission management in IoHT, in addition to some common challenges identified, such as privacy, resource constraints, and data heterogeneity. Furthermore, we examine the existing technologies that can benefit BlockFL, thereby helping researchers and practitioners to make informed decisions about the selection and development of BlockFL for various IoT application scenarios.

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arXiv2023

A Referable NFT Scheme

Existing NFTs confront restrictions of \textit{one-time incentive} and \textit{product isolation}. Creators cannot obtain benefits once having sold their NFT products due to the lack of relationships across different NFTs, which results in controversial possible profit sharing. This work proposes a referable NFT scheme to extend the incentive sustainability of NFTs. We construct the referable NFT (rNFT) network to increase exposure and enhance the referring relationship of inclusive items. We introduce the DAG topology to generate directed edges between each pair of NFTs with corresponding weights and labels for advanced usage. We accordingly implement and propose the scheme under Ethereum Improvement Proposal (EIP) standards, indexed in EIP-1155. Further, we provide the mathematical formation to analyze the utility for each rNFT participant. The discussion gives general guidance among multi-dimensional parameters. To our knowledge, this is the first study to build the referable NFT network, explicitly showing the virtual connections among NFTs.

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arXiv2023

Leveraging Architectural Approaches in Web3 Applications -- A DAO Perspective Focused

Architectural design contexts contain a set of factors that influence software application development. Among them, organizational design contexts consist of high-level company concerns and how it is structured, for example, stakeholders and development schedule, heavily impacting design considerations. Decentralized Autonomous Organization (DAO), as a vital concept in the Web3 space, is an organization constructed by automatically executed rules such as via smart contracts, holding features of the permissionless committee, transparent proposals, and fair contribution by stakeholders. In this work, we conduct a systematic literature review to summarize how DAO is structured as well as explore its benefits&challenges in Web3 applications.

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arXiv2023

IronForge: An Open, Secure, Fair, Decentralized Federated Learning

Federated learning (FL) provides an effective machine learning (ML) architecture to protect data privacy in a distributed manner. However, the inevitable network asynchrony, the over-dependence on a central coordinator, and the lack of an open and fair incentive mechanism collectively hinder its further development. We propose \textsc{IronForge}, a new generation of FL framework, that features a Directed Acyclic Graph (DAG)-based data structure and eliminates the need for central coordinators to achieve fully decentralized operations. \textsc{IronForge} runs in a public and open network, and launches a fair incentive mechanism by enabling state consistency in the DAG, so that the system fits in networks where training resources are unevenly distributed. In addition, dedicated defense strategies against prevalent FL attacks on incentive fairness and data privacy are presented to ensure the security of \textsc{IronForge}. Experimental results based on a newly developed testbed FLSim highlight the superiority of \textsc{IronForge} to the existing prevalent FL frameworks under various specifications in performance, fairness, and security. To the best of our knowledge, \textsc{IronForge} is the first secure and fully decentralized FL framework that can be applied in open networks with realistic network and training settings.

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arXiv2022

BGRA: A Reference Architecture for Blockchain Governance

Blockchain technology has been integrated into diverse software applications by enabling a decentralised architecture design. However, the defects of on-chain algorithmic mechanisms, and tedious disputes and debates in off-chain communities may affect the operation of blockchain systems. Accordingly, blockchain governance has received great interest for supporting the design, use, and maintenance of blockchain systems, hence improving the overall trustworthiness. Although much effort has been put into this research topic, there is a distinct lack of consideration for blockchain governance from the perspective of software architecture design. In this study, we propose a pattern-oriented reference architecture for governance-driven blockchain systems, which can provide guidance for future blockchain architecture design. We design the reference architecture based on an extensive review of architecture patterns for blockchain governance in academic literature and industry implementation. The reference architecture consists of four layers. We demonstrate the components in each layer, annotating with the identified patterns. A qualitative analysis of mapping two concrete blockchain architectures, Polkadot and Quorum, on the reference architecture is conducted, to evaluate the correctness and utility of proposed reference architecture.

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arXiv2022

A Pattern Language for Blockchain Governance

Blockchain technology has been used to build next-generation applications taking advantage of its decentralised nature. Nevertheless, there are some serious concerns about the trustworthiness of blockchain due to the vulnerabilities in on-chain algorithmic mechanisms, and tedious disputes and debates in off-chain communities. Accordingly, blockchain governance has received great attention for improving the trustworthiness of all decisions that direct a blockchain platform. However, there is a lack of systematic knowledge to guide practitioners to perform blockchain governance. We have performed a systematic literature review to understand the state-of-the-art of blockchain governance. We identify the lifecycle stages of a blockchain platform, and present 14 architectural patterns for blockchain governance in this study. This pattern language can provide guidance for the effective use of patterns for blockchain governance in practice, and support the architecture design of governance-driven blockchain systems.

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arXiv2022

Tamperproof IoT with Blockchain

We investigate the tamper-resistant property of Blockchain and its effectiveness for IoT systems. In particular, we implemented an IoT testbed, and built a Blockchain into the testbed. A number of tamper-resistance experiments were conducted and analyzed to corroborate the process of block validation in Blockchain. Our analysis and experimental results demonstrate the tamper-resistant capability of Blockchain in securing trust in IoT systems. The demonstration video is provided at [1].

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arXiv2022

Defining Blockchain Governance Principles: A Comprehensive Framework

Blockchain eliminates the need for trusted third-party intermediaries in business by enabling decentralised architecture design in software applications. However, the vulnerabilities in on-chain autonomous decision-makings and cumbersome off-chain coordination lead to serious concerns about blockchain's ability to behave in a trustworthy and efficient way. Blockchain governance has received considerable attention to support the decision-making process during the use and evolution of blockchain. Nevertheless, the conventional governance frameworks do not apply to blockchain due to its distributed architecture and decentralised decision process. These inherent features lead to the absence of a clear source of authority in blockchain ecosystem. Currently, there is a lack of systematic guidance on the governance of blockchain. Therefore, in this paper, we present a comprehensive blockchain governance framework, which elucidates an integrated view of the degree of decentralisation, decision rights, incentives, accountability, ecosystem, and legal and ethical responsibilities. The above aspects are formulated as six high-level principles for blockchain governance. We demonstrate a qualitative analysis of the proposed framework, including case studies on five extant blockchain platforms, and comparison with existing blockchain governance frameworks. The results show that our proposed framework is feasible and applicable in a real-world context.

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arXiv2021

Capacity Analysis of Public Blockchain

As distributed ledgers, blockchains run consensus protocols which trade capacity for consistency, especially in non-ideal networks with incomplete connectivity and erroneous links. Existing studies on the tradeoff between capacity and consistency are only qualitative or rely on specific assumptions. This paper presents discrete-time Markov chain models to quantify the capacity of Proof-of-Work based public blockchains in non-ideal networks. The comprehensive model is collapsed to be ergodic under the eventual consistency of blockchains, achieving tractability and efficient evaluations of blockchain capacity. A closed-form expression for the capacity is derived in the case of two miners. Another important aspect is that we extend the ergodic model to analyze the capacity under strong consistency, evaluating the robustness of blockchains against double-spending attacks. Validated by simulations, the proposed models are accurate and reveal the effect of link quality and the distribution of mining rates on blockchain capacity and the ratio of stale blocks.

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