Browsing by Author "Xu, Minghui"

Now showing 1 - 5 of 5
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    A trustless architecture of blockchain-enabled metaverse
    (Elsevier, 2023-03) Xu, Minghui; Guo, Yihao; Hu, Qin; Xiong, Zehui; Yu, Dongxiao; Cheng, Xuizhen; Computer and Information Science, School of Science
    Metaverse has rekindled human beings’ desire to further break space-time barriers by fusing the virtual and real worlds. However, security and privacy threats hinder us from building a utopia. A metaverse embraces various techniques, while at the same time inheriting their pitfalls and thus exposing large attack surfaces. Blockchain, proposed in 2008, was regarded as a key building block of metaverses. it enables transparent and trusted computing environments using tamper-resistant decentralized ledgers. Currently, blockchain supports Decentralized Finance (DeFi) and Non-fungible Tokens (NFT) for metaverses. However, the power of a blockchain has not been sufficiently exploited. In this article, we propose a novel trustless architecture of blockchain-enabled metaverse, aiming to provide efficient resource integration and allocation by consolidating hardware and software components. To realize our design objectives, we provide an On-Demand Trusted Computing Environment (OTCE) technique based on local trust evaluation. Specifically, the architecture adopts a hypergraph to represent a metaverse, in which each hyperedge links a group of users with certain relationship. Then the trust level of each user group can be evaluated based on graph analytics techniques. Based on the trust value, each group can determine its security plan on demand, free from interference by irrelevant nodes. Besides, OTCEs enable large-scale and flexible application environments (sandboxes) while preserving a strong security guarantee.
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    Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing
    (IEEE Xplore, 2021-11) Hu, Qin; Wang, Zhilin; Xu, Minghui; Cheng, Xiuzhen; Computer and Information Science, School of Science
    Mobile crowdsensing (MCS) counting on the mobility of massive workers helps the requestor accomplish various sensing tasks with more flexibility and lower cost. However, for the conventional MCS, the large consumption of communication resources for raw data transmission and high requirements on data storage and computing capability hinder potential requestors with limited resources from using MCS. To facilitate the widespread application of MCS, we propose a novel MCS learning framework leveraging on blockchain technology and the new concept of edge intelligence based on federated learning (FL), which involves four major entities, including requestors, blockchain, edge servers and mobile devices as workers. Even though there exist several studies on blockchain-based MCS and blockchain-based FL, they cannot solve the essential challenges of MCS with respect to accommodating resource-constrained requestors or deal with the privacy concerns brought by the involvement of requestors and workers in the learning process. To fill the gaps, four main procedures, i.e., task publication, data sensing and submission, learning to return final results, and payment settlement and allocation, are designed to address major challenges brought by both internal and external threats, such as malicious edge servers and dishonest requestors. Specifically, a mechanism design based data submission rule is proposed to guarantee the data privacy of mobile devices being truthfully preserved at edge servers; consortium blockchain based FL is elaborated to secure the distributed learning process; and a cooperation-enforcing control strategy is devised to elicit full payment from the requestor. Extensive simulations are carried out to evaluate the performance of our designed schemes.
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    Blockchain-based Edge Resource Sharing for Metaverse
    (IEEE, 2022-10) Wang, Zhilin; Hut, Qin; Xu, Minghui; Jiang, Honglu; Computer and Information Science, School of Science
    Although Metaverse has recently been widely studied, its practical application still faces many challenges. One of the severe challenges is the lack of sufficient resources for computing and communication on local devices, resulting in the inability to access the Metaverse services. To address this issue, this paper proposes a practical blockchain-based mobile edge computing (MEC) platform for resource sharing and optimal utilization to complete the requested offloading tasks, given the heterogeneity of servers' available resources and that of users' task requests. To be specific, we first elaborate the design of our proposed system and then dive into the task allocation mechanism to assign offloading tasks to proper servers. To solve the multiple task allocation (MTA) problem in polynomial time, we devise a learning-based algorithm. Since the objective function and constraints of MTA are significantly affected by the servers uploading the tasks, we reformulate it as a reinforcement learning problem and calculate the rewards for each state and action considering the influences of servers. Finally, numerous experiments are conducted to demonstrate the effectiveness and efficiency of our proposed system and algorithms.
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    Quantum Analysis on Task Allocation and Quality Control for Crowdsourcing With Homogeneous Workers
    (IEEE, 2020-05) Xu, Minghui; Wang, Shengling; Hu, Qin; Sheng, Hao; Cheng, Xiuzhen; Computer and Information Science, School of Science
    Crowdsourcing has been emerging as a valid problem-solving model that harnesses a large group of contributors to solve a complicated task. However, existing crowdsourcing platforms or systems could suffer from task allocation and quality control problems. In this article, we first prove that there exist two dilemmas while tackling the above issues by using a game-theoretic approach. To overcome this challenge, we are focusing on exploiting quantum crowdsourcing schemes in which the welfare of requestor or worker can be maximized since quantum players share the extended strategy space, and the introduction of entanglement offers a new method of depicting fine-grained relations between players. Specifically, we propose a quantum game model for quota-oriented crowdsourcing game to address dilemmas in task allocation. The result indicates the dilemma based on classical strategy will disappear with the increment of entanglement degree. While in the quality-oriented crowdsourcing game, we adopt a density matrix approach to calculate the optimal payoffs of both sides, which demonstrates the superiority of our quantum strategy. Moreover, our quantum scheme is generic since it is compatible with the schemes from a classical perspective. Hence, our noteworthy quantum crowdsourcing schemes offer a promising alternative route for tackling dilemmas in crowdsourcing scenarios.
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    zk-PCN: A Privacy-Preserving Payment Channel Network Using zk-SNARKs
    (IEEE, 2022-11) Yu, Wenxuan; Xu, Minghui; Yu, Dongxiao; Cheng, Xiuzhen; Hu, Qin; Xiong, Zehui; Computer and Information Science, School of Science
    Payment channel network (PCN) is a layer-two scaling solution that enables fast off-chain transactions but does not involve on-chain transaction settlement. PCNs raise new privacy issues including balance secrecy, relationship anonymity and payment privacy. Moreover, protecting privacy causes low transaction success rates. To address this dilemma, we propose zk-PCN, a privacy-preserving payment channel network using zk-SNARKs. We prevent from exposing true balances by setting up public balances instead. Using public balances, zk-PCN can guarantee high transaction success rates and protect PCN privacy with zero-knowledge proofs. Additionally, zk-PCN is compatible with the existing routing algorithms of PCNs. To support such compatibility, we propose zk-IPCN to improve zk-PCN with a novel proof generation (RPG) algorithm. zk-IPCN reduces the overheads of storing channel information and lowers the frequency of generating zero-knowledge proofs. Finally, extensive simulations demonstrate the effectiveness and efficiency of zk-PCN in various settings.