Browsing by Author "Wang, Shengling"

Now showing 1 - 8 of 8
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    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.
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    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.
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    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.
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    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.
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    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.
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    Privacy-Aware Data Trading
    (IEEE Xplore, 2021-07) Wang, Shengling; Shi, Lina; Hu, Qin; Zhang, Junshan; Cheng, Xiuzhen; Yu, Jiguo; Computer and Information Science, School of Science
    The growing threat of personal data breach in data trading pinpoints an urgent need to develop countermeasures for preserving individual privacy. The state-of-the-art work either endows the data collector with the responsibility of data privacy or reports only a privacy-preserving version of the data. The basic assumption of the former approach that the data collector is trustworthy does not always hold true in reality, whereas the latter approach reduces the value of data. In this paper, we investigate the privacy leakage issue from the root source. Specifically, we take a fresh look to reverse the inferior position of the data provider by making her dominate the game with the collector to solve the dilemma in data trading. To that aim, we propose the noisy-sequentially zero-determinant (NSZD) strategies by tailoring the classical zero-determinant strategies, originally designed for the simultaneous-move game, to adapt to the noisy sequential game. NSZD strategies can empower the data provider to unilaterally set the expected payoff of the data collector or enforce a positive relationship between her and the data collector's expected payoffs. Both strategies can stimulate a rational data collector to behave honestly, boosting a healthy data trading market. Numerical simulations are used to examine the impacts of key parameters and the feasible region where the data provider can be an NSZD player. Finally, we prove that the data collector cannot employ NSZD to further dominate the data market for deteriorating privacy leakage.
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    Proof of Federated Learning: A Novel Energy-Recycling Consensus Algorithm
    (IEEE Xplore, 2021-08) Qu, Xidi; Wang, Shengling; Hu, Qin; Cheng, Xiuzhen; Computer and Information Science, School of Science
    Proof of work (PoW), the most popular consensus mechanism for blockchain, requires ridiculously large amounts of energy but without any useful outcome beyond determining accounting rights among miners. To tackle the drawback of PoW, we propose a novel energy-recycling consensus algorithm, namely proof of federated learning (PoFL), where the energy originally wasted to solve difficult but meaningless puzzles in PoW is reinvested to federated learning. Federated learning and pooled-mining, a trend of PoW, have a natural fit in terms of organization structure. However, the separation between the data usufruct and ownership in blockchain lead to data privacy leakage in model training and verification, deviating from the original intention of federal learning. To address the challenge, a reverse game-based data trading mechanism and a privacy-preserving model verification mechanism are proposed. The former can guard against training data leakage while the latter verifies the accuracy of a trained model with privacy preservation of the task requester's test data as well as the pool's submitted model. To the best of our knowledge, our article is the first work to employ federal learning as the proof of work for blockchain. Extensive simulations based on synthetic and real-world data demonstrate the effectiveness and efficiency of our proposed mechanisms.
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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.