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Browsing by Author "Hu, Qin"
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Item 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 ScienceMetaverse 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.Item Alliance Makes Difference? Maximizing Social Welfare in Cross-Silo Federated Learning(IEEE, 2024-02) Chen, Jianan; Hu, Qin; Jiang, Honglu; Computer and Information Science, Purdue School of ScienceAs one of the typical settings of Federated Learning (FL), cross-silo FL allows organizations to jointly train an optimal Machine Learning (ML) model. In this case, some organizations may try to obtain the global model without contributing their local training power, lowering the social welfare. In this article, we model the interactions among organizations in cross-silo FL as a public goods game and theoretically prove that there exists a social dilemma where the maximum social welfare is not achieved in Nash equilibrium. To overcome this dilemma, we employ the Multi-player Multi-action Zero-Determinant (MMZD) strategy to maximize the social welfare. With the help of the MMZD, an individual organization can unilaterally control the social welfare without extra cost. Since the MMZD strategy can be adopted by all organizations, we further study the case of multiple organizations jointly adopting the MMZD strategy to form an MMZD Alliance (MMZDA). We prove that the MMZDA strategy can strengthen the control of the maximum social welfare. Experimental results validate that the MMZD strategy is effective in obtaining the maximum social welfare and the MMZDA can achieve a larger maximum value.Item An Adaptive and Modular Blockchain Enabled Architecture for a Decentralized Metaverse(IEEE, 2024-04) Cheng, Ye; Guo, Yihao; Xu, Minghui; Hu, Qin; Yu, Dongxiao; Cheng, Xiuzhen; Computer Information and Graphics Technology, Purdue School of Engineering and TechnologyA metaverse breaks the boundaries of time and space between people, realizing a more realistic virtual experience, improving work efficiency, and creating a new business model. Blockchain, as one of the key supporting technologies for a metaverse design, provides a trusted interactive environment. However, the rich and varied scenes of a metaverse have led to excessive consumption of on-chain resources, raising the threshold for ordinary users to join, thereby losing the human-centered design. Therefore, we propose an adaptive and modular blockchain-enabled architecture for a decentralized metaverse to address these issues. The solution includes an adaptive consensus/ledger protocol based on a modular blockchain, which can effectively adapt to the ever-changing scenarios of the metaverse, reduce resource consumption, and provide a secure and reliable interactive environment. In addition, we propose the concept of Non-Fungible Resource (NFR) to virtualize idle resources. Users can establish a temporary trusted environment and rent others’ NFR to meet their computing needs. Finally, we simulate and test our solution based on XuperChain, and the experimental results prove the feasibility of our design.Item An Uncertainty- and Collusion-Proof Voting Consensus Mechanism in Blockchain(IEEE, 2023-10) Wang, Shengling; Qu, Xidi; Hu, Qin; Wang, Xia; Cheng, Xiuzhen; Computer Science, Luddy School of Informatics, Computing, and EngineeringThough voting-based consensus algorithms in blockchain outperform proof-based ones in energy- and transaction-efficiency, they are prone to incur wrong elections and bribery elections. The former originates from the uncertainties of candidates’ capability and availability, and the latter comes from the egoism of voters and candidates. Hence, in this paper, we propose an uncertainty- and collusion-proof voting consensus mechanism, including the selection pressure-based voting algorithm and the trustworthiness evaluation algorithm. The first algorithm can decrease the side effects of candidates’ uncertainties, lowering wrong elections while trading off the balance between efficiency and fairness in voting miners. The second algorithm adopts an incentive-compatible scoring rule to evaluate the trustworthiness of voting, motivating voters to report true beliefs on candidates by making egoism consistent with altruism so as to avoid bribery elections. A salient feature of our work is theoretically analyzing the proposed voting consensus mechanism by the large deviation theory. Our analysis provides not only the voting failure rate of a candidate but also its decay speed. The voting failure rate measures the incompetence of any candidate from a personal perspective by voting, based on which the concepts of the effective selection valve and the effective expectation of merit are introduced to help the system designer determine the optimal voting standard and guide a candidate to behave in an optimal way for lowering the voting failure rate.Item Black Swan in Blockchain: Micro Analysis of Natural Forking(IEEE, 2022-11-04) Shi, Hongwei; Wang, Shengling; Hu, Qin; Cheng, Xiuzhen; Computer and Information Science, School of ScienceNatural forking is tantamount to the “black swan” event in blockchain since it emerges unexpectedly with a small probability, and may incur low resource utilization and costly economic loss. The ongoing literature analyzes natural forking mainly from the macroscopic perspective, which is insufficient to further understand this phenomenon since it roots in the instantaneous difference between block creation and propagation microscopically. Hence, in this paper, we fill this gap by leveraging the large deviation theory to conduct the first micro study of natural forking, aiming to reveal its inherent mechanism substantially. Our work is featured by 1) conceptual innovation . We creatively abstract the blockchain overlay network as a “service system”. This allows us to investigate natural forking from the perspective of “supply and demand”. Based on this, we can identify the competitive dynamics of blockchain and construct a queuing model to characterize natural forking; 2) progressiveness . We scrutinize the natural forking probability as well as its decay rate via a three-step scheme from simple to complex, which are the single-source i.i.d. scheme, the single-source non-i.i.d. scheme, and the many-source non-i.i.d. scheme. By doing so, we can answer when and how fast should we take actions and what actions should we take against natural forking. Our valuable findings can not only put forward decisive guidelines theoretically from the top level, but also engineer optimal countermeasures operationally on a practical level to thwart natural forking.Item 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 ScienceMobile 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.Item CommandFence: A Novel Digital-Twin-Based Preventive Framework for Securing Smart Home Systems(IEEE, 2023-05) Xiao, Yinhao; Jia, Yizhen; Hu, Qin; Cheng, Xiuzhen; Gong, Bei; Yu, Jiguo; Computer and Information Science, School of ScienceSmart home systems are both technologically and economically advancing rapidly. As people become gradually inalienable to smart home infrastructures, their security conditions are getting more and more closely tied to everyone's privacy and safety. In this paper, we consider smart apps, either malicious ones with evil intentions or benign ones with logic errors, that can cause property loss or even physical sufferings to the user when being executed in a smart home environment and interacting with human activities and environmental changes. Unfortunately, current preventive measures rely on permission-based access control, failing to provide ideal protections against such threats due to the nature of their rigid designs. In this paper, we propose CommandFence, a novel digital-twin-based security framework that adopts a fundamentally new concept of protecting the smart home system by letting any sequence of app commands to be executed in a virtual smart home system, in which a deep-q network (DQN) is used to predict if the sequence could lead to a risky consequence. CommandFence is composed of an Interposition Layer to interpose app commands and an Emulation Layer to figure out whether they can cause any risky smart home state if correlating with possible human activities and environmental changes. We fully implemented our CommandFence implementation and tested against 553 official SmartApps on the Samsung SmartThings platform and successfully identified 34 potentially dangerous ones, with 31 of them reported to be problematic Author: Please provide index terms/keywords for your article. To download the IEEE Taxonomy go to http://www.ieee.org/documents/taxonomy_v101.pdf ?> the first time to our best knowledge. Moreover, We tested our CommandFence on the 10 malicious SmartApps created by Jia et al. 2017, and successfully identified 7 of them as risky, with the missed ones actually only causing smartphone information leak (not harmful to the smart home system). We also tested CommandFence against the 17 benign SmartApps with logic errors developed by Celik et al. 2017, and achieved a 100% accuracy. Our experimental studies indicate that adopting CommandFence incurs a neglectable overhead of 0.1675 seconds.Item A Correlated Equilibrium based Transaction Pricing Mechanism in Blockchain(IEEE, 2020-05) Hu, Qin; Nigam, Yash; Wang, Zhilin; Wang, Yawei; Xiao, Yinhao; Computer and Information Science, School of ScienceAlthough transaction fees are not obligatory in most of the current blockchain systems, extensive studies confirm their importance in maintaining the security and sustainability of blockchain. To enhance blockchain in the long term, it is crucial to design effective transaction pricing mechanisms. Different from the existing schemes based on auctions with more consideration about the profit of miners, we resort to game theory and propose a correlated equilibrium based transaction pricing mechanism through solving a pricing game among users with transactions, which can achieve both the individual and global optimum. To avoid the computational complexity exponentially increasing with the number of transactions, we further improve the game-theoretic solution with an approximate algorithm, which can derive almost the same results as the original one but costs significantly reduced time. Experimental results demonstrate the effectiveness and efficiency of our proposed mechanism.Item Cost-Efficient Mobile Crowdsensing with Spatial-Temporal Awareness(IEEE, 2019-11) Hu, Qin; Wang, Shengling; Cheng, Xiuzhen; Zhang, Junshan; Lv, Weifeng; Computer and Information Science, School of ScienceA cost-efficient deal that can achieve high sensing quality with a low reward is the permanent goal of the requestor in mobile crowdsensing, which heavily depends on the quantity and quality of the workers. However, spatial diversity and temporal dynamics lead to heterogeneous worker supplies, making it hard for the requestor to utilize a homogeneous pricing strategy to realize a cost-efficient deal from a systematic point of view. Therefore, a cost-efficient deal calls for a cost-efficient pricing strategy, boosting the whole sensing quality with less operation (computation) cost. However, state-of-the-art studies ignore the dual cost-efficient demands of large-scale sensing tasks. Hence, we propose a combinatorial pinning zero-determinant (ZD) strategy, which empowers the requestor to utilize a single strategy within its feasible range to minimize the total expected utilities of the workers throughout all sensing regions for each time interval, without being affected by the strategies of the workers. Through turning the worker-customized strategy to an interval-customized one, the proposed combinatorial pinning ZD strategy reduces the number of pricing strategies required by the requestor from O(n^3)to O(n)$ . Besides, it extends the application scenarios of the classical ZD strategy from two-player simultaneous-move games to multiple-heterogeneous-player sequential-move ones, where a leader can determine the linear relationship of the players' expected utilities.Item Defense Strategies Toward Model Poisoning Attacks in Federated Learning: A Survey(IEEE, 2022-04) Wang, Zhilin; Kang, Qiao; Zhang, Xinyi; Hu, Qin; Computer and Information Science, School of ScienceAdvances in distributed machine learning can empower future communications and networking. The emergence of federated learning (FL) has provided an efficient framework for distributed machine learning, which, however, still faces many security challenges. Among them, model poisoning attacks have a significant impact on the security and performance of FL. Given that there have been many studies focusing on defending against model poisoning attacks, it is necessary to survey the existing work and provide insights to inspire future research. In this paper, we first classify defense mechanisms for model poisoning attacks into two categories: evaluation methods for local model updates and aggregation methods for the global model. Then, we analyze some of the existing defense strategies in detail. We also discuss some potential challenges and future research directions. To the best of our knowledge, we are the first to survey defense methods for model poisoning attacks in FL.