IU Indianapolis ScholarWorks :: Browsing by Author "Cheng, Xiuzhen"

Browsing by Author "Cheng, Xiuzhen"

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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 Technology
A 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.
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 Engineering
Though 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.
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 Science
Natural 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.
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.
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 Science
Smart 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.
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 Science
A 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.
A game theoretic analysis on block withholding attacks using the zero-determinant strategy
(ACM, 2019-06) Hu, Qin; Wang, Shengling; Cheng, Xiuzhen; Computer and Information Science, School of Science
In Bitcoin's incentive system that supports open mining pools, block withholding attacks incur huge security threats. In this paper, we investigate the mutual attacks among pools as this determines the macroscopic utility of the whole distributed system. Existing studies on pools' interactive attacks usually employ the conventional game theory, where the strategies of the players are considered pure and equal, neglecting the existence of powerful strategies and the corresponding favorable game results. In this study, we take advantage of the Zero-Determinant (ZD) strategy to analyze the block withholding attack between any two pools, where the ZD adopter has the unilateral control on the expected payoffs of its opponent and itself. In this case, we are faced with the following questions: who can adopt the ZD strategy? individually or simultaneously? what can the ZD player achieve? In order to answer these questions, we derive the conditions under which two pools can individually or simultaneously employ the ZD strategy and demonstrate the effectiveness. To the best of our knowledge, we are the first to use the ZD strategy to analyze the block withholding attack among pools.
A Misreport- and Collusion-Proof Crowdsourcing Mechanism Without Quality Verification
(IEEE Xplore, 2022-01) Li, Kun; Wang, Shengling; Cheng, Xiuzhen; Hu, Qin; Computer and Information Science, School of Science
Quality control plays a critical role in crowdsourcing. The state-of-the-art work is not suitable for crowdsourcing applications that require extensive validation of the tasks quality, since it is a long haul for the requestor to verify task quality or select professional workers in a one-by-one mode. In this paper, we propose a misreport- and collusion-proof crowdsourcing mechanism, guiding workers to truthfully report the quality of submitted tasks without collusion by designing a mechanism, so that workers have to act the way the requestor would like. In detail, the mechanism proposed by the requester makes no room for the workers to obtain profit through quality misreport and collusion, and thus, the quality can be controlled without any verification. Extensive simulation results verify the effectiveness of the proposed mechanism. Finally, the importance and originality of our work lie in that it reveals some interesting and even counterintuitive findings: 1) a high-quality worker may pretend to be a low-quality one; 2) the rise of task quality from high-quality workers may not result in the increased utility of the requestor; 3) the utility of the requestor may not get improved with the increasing number of workers. These findings can boost forward looking and strategic planning solutions for crowdsourcing.
Nothing Wasted: Full Contribution Enforcement in Federated Edge Learning
(IEEE Xplore, 2021-10) Hu, Qin; Wang, Shengling; Xiong, Zehui; Cheng, Xiuzhen; Computer and Information Science, School of Science
The explosive amount of data generated at the network edge makes mobile edge computing an essential technology to support real-time applications, calling for powerful data processing and analysis provided by machine learning (ML) techniques. In particular, federated edge learning (FEL) becomes prominent in securing the privacy of data owners by keeping the data locally used to train ML models. Existing studies on FEL either utilize in-process optimization or remove unqualified participants in advance. In this paper, we enhance the collaboration from all edge devices in FEL to guarantee that the ML model is trained using all available local data to accelerate the learning process. To that aim, we propose a collective extortion (CE) strategy under the imperfect-information multi-player FEL game, which is proved to be effective in helping the server efficiently elicit the full contribution of all devices without worrying about suffering from any economic loss. Technically, our proposed CE strategy extends the classical extortion strategy in controlling the proportionate share of expected utilities for a single opponent to the swiftly homogeneous control over a group of players, which further presents an attractive trait of being impartial for all participants. Both theoretical analysis and experimental evaluations validate the effectiveness and fairness of our proposed scheme.
Online Learning for Failure-Aware Edge Backup of Service Function Chains With the Minimum Latency
(IEEE, 2023-12) Wang, Chen; Hu, Qin; Yu, Dongxiao; Cheng, Xiuzhen; Computer Science, Luddy School of Informatics, Computing, and Engineering
Virtual network functions (VNFs) have been widely deployed in mobile edge computing (MEC) to flexibly and efficiently serve end users running resource-intensive applications, which can be further serialized to form service function chains (SFCs), providing customized networking services. To ensure the availability of SFCs, it turns out to be effective to place redundant SFC backups at the edge for quickly recovering from any failures. The existing research largely overlooks the influences of SFC popularity, backup completeness, and failure rate on the optimal deployment of SFC backups on edge servers. In this paper, we comprehensively consider from the perspectives of both the end users and edge system to backup SFCs for providing popular services with the lowest latency. To overcome the challenges resulted from unknown SFC popularity and failure rate, as well as the known system parameter constraints, we take advantage of the online bandit learning technique to cope with the uncertainty issue. Combining the Prim -inspired method with the greedy strategy, we propose a Real-Time Selection and Deployment (RTSD) algorithm. Extensive simulation experiments are conducted to demonstrate the superiority of our proposed algorithms.

Browsing by Author "Cheng, Xiuzhen"

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