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当期目录

    15 July 2022, Volume 8 Issue 4
    TOPIC: DIGITAL ECONOMY
    Digital Economy
    2022, 8(4):  1-2.  doi:10.11959/j.issn.2096-0271.2022062-1
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    Value chain model of data governance and its application on data governance regulation analysis
    Keman HUANG, Xiaoyong DU
    2022, 8(4):  3-16.  doi:10.11959/j.issn.2096-0271.2022062
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    Cultivating the data marketplace is an important mechanism to achieve the value of big data.The prosperity of such a data marketplace needs a sustainable and healthy data service ecosystem.A data governance value chain model was developed to identify the primary and support activities for data value release.Then the data service ecosystem model was implemented accordingly to distinguish different stakeholders and their core functions that a data marketplace should have.Using the developed data governance value chain model and data service ecosystem model, the data dovernance regulation was analyzed systematically, aiming at providing suggestions to promote the growth of the data marketplace.

    Research on reform of governance system around the risk of digital platform companies
    Zerui ZHAO
    2022, 8(4):  17-33.  doi:10.11959/j.issn.2096-0271.2022063
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    As the digital economy is gradually becoming an important support for the development of the global economy, the risk management of digital platform companies will be an important factor affecting the future economic development of various countries.Based on the analysis of the concept of risk governance and the tracing of corporate governance theory, the governance dilemma of digital platform companies was pointed out.The governance system reform ideas and specific risk governance policies of China, Japan and the United States, had been sorted out.The governance system reforms of the three countries were compared.The consensus on governance reform of countries to build risk communication channels among multi stakeholders had been refined.Finally, the obstacles and solutions faced by the regional cooperation and development of digital economy in East Asia and the world were pointed out.

    Modus operandi of big data governance: some preliminary observations
    Wenlong LI, Yuan YUAN, Xiaopeng AN
    2022, 8(4):  34-45.  doi:10.11959/j.issn.2096-0271.2022077
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    The modus operandi of big data governance was considered, and a typology was provided, which included simple analogies, legal transplantation, and regulatory imagination.Neither contradictory nor mutually exclusionary, all these modus may co-exist at a certain period on a continuous spectrum.To aid the reader’s understanding, the concept of path dependence was used as a point of reference.The first two modus operandi was essentially path-dependent whereas the third was characterized by detaching from any existing paths or inertial thinking.Based on this quasi-methodological account, some high-level recommendations were provided for big data governance.Firstly, as a means of governance, property thinking had been stretched to the logical point of exhaustion.It was of prime importance when and how derails from it while genuinely designing rules around data practices distinct from tangible goods exchange.Secondly, legal transplantation has been the engine for big data governance.Yet, transplantation was far more sophisticated than mere legal translation, and the former was more of a means rather than an end.Lastly, in contrast to the previous two path-dependent methods, a third modus operandi i.e., “regulatory imagination” was proposed, which stressed the urgent need to detach from any outdated paths or thinking.Building imaginative capacity was a complex and lengthy process, requiring interdisciplinary thoughts, cognition rooted in practices rather than concepts, and effective rule-theory interaction.

    Regulatory thinking and practice of financial business in the field of platform economy in digital economy era
    Dejun WANG, Yanan DAI
    2022, 8(4):  46-55.  doi:10.11959/j.issn.2096-0271.2022065
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    In the era of the digital economy, financial business in the field of the platform economy is developing continuously, which has greatly enhanced the support of finance to the real economy.However, the prominent problems of market monopoly and data security in the financial business in the field of platform economy have brought risks to the orderly development of financial business in the field of the platform economy, and also brought new challenges to supervision.In the face of unhealthy and irregular signs and trends in the rapid development of financial business in the field of the platform economy, relevant departments of the state put forward the need to resolutely correct and govern.Starting from the regulatory side, through the observation platform economy enterprise financial business platform for the evolution process and 14 representative enterprises present situation of the financial business, to analyze the risk of economic enterprise financial business platform and regulatory weaknesses, technics of regulation technology was put forward to construct financial risk evaluation model, the first time found risk behavior and hidden dangers, timely warning.The transformation of platform economic regulation will be promoted into active regulation, penetrating regulation, and consistent regulation.

    Data and algorithm security in platform economy
    Tingyi ZHENG, Liang PANG, Xiaolong JIN
    2022, 8(4):  56-66.  doi:10.11959/j.issn.2096-0271.2022075
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    Artificial intelligence, big data, and other technologies are rapidly promoting the development of the platform economy.While Internet platforms use the two-wheel mechanism of “data + algorithm” to provide users with accurate and personalized information services, they also have fair competition for national network security and market due to the platform’s “ecological monopoly” and irregular application of algorithms.The legitimate interests of users pose a threat and bring serious challenges to government supervision and platform governance.Under this background, the connotation, extension, and research status of data and algorithm security in the platform economy were discussed, and the technical system and key technical problems that promote the innovation and development of the platform under the premise of ensuring security were sorted out, and the promotion suggestions for ecological innovation and development of the platform economy from four aspects were given: architecture construction, perfection of supervison systems, breakthrough of core technology, and platform model innovation.

    Exchange mechanism for decentralized finance: a survey
    Yimin DENG, Shijing SI, Jianzong WANG, Zeyuan LI, Jing XIAO
    2022, 8(4):  67-84.  doi:10.11959/j.issn.2096-0271.2022064
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    Decentralized finance (DeFi) is a new paradigm for providing financial services based on blockchain and smart contract, which support many applications including loans and derivatives.Therefore, the exchange mechanism of DeFi has attracted large amount of attention, as it directly affects the stability of upper-level applications.The exchange mechanism of DeFi was reviewed.Firstly, the concept and the protocols related to exchange mechanism were introduced.Secondly, the transaction mechanism was classified through their approaches of realization, and the methods based on order book, automated market maker and aggregator were discussed respectively.The differences and connections among the implementation of those methods were introduced.Finally, the fairness, security and anonymity problems faced by the decentralized exchange were analyzed and summarized, and potential future research directions were proposed.

    Research on the economic analytical model of three-tier data factor market
    Zhipeng LU
    2022, 8(4):  85-93.  doi:10.11959/j.issn.2096-0271.2022070
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    Although the establishment of the data factor market follows the general law of traditional marketoriented allocation of production factors, there still exists some unsolved problems such as unclear object of the transaction, imperfect pricing and transaction mechanisms, etc.By segmenting the data factor market, a three-tier market system for the different objects of data transaction was constructed and the dominant factors affecting the release of the data value in each market were further analyzed.Through the cost-benefit analysis of market entities, the economic analytical model of the data factor market was established and the economic advantages of the three-tier market were confirmed by data factories applying local practice cases.

    Analysis on various patterns of data intermediary
    Zhenhua LI, Tongyi WANG
    2022, 8(4):  94-104.  doi:10.11959/j.issn.2096-0271.2022068
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    Data intermediaries are expected to become the backbone of promoting data circulation through diversified innovative practices.Various patterns of data intermediaries were introduced.Different data intermediaries focus on solving different practical problems.For example, the data transaction platform focused on solving the information asymmetry between the supply and demand sides, open banking service providers such as Plaid focused on the unified conversion of data standards and data interfaces, and data trust could optimize the personal information sharing the path of authorization and consent.It was suggested to adhere to the problem orientation, actively explore and innovate diversified data intermediary patterns according to the needs of the scenario, build a healthy and reliable data ecosystem, and fully release the value of data.

    STUDY
    Survey on federated recommendation systems
    Zhitao ZHU, Shijing SI, Jianzong WANG, Jing XIAO
    2022, 8(4):  105-132.  doi:10.11959/j.issn.2096-0271.2022032
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    In the federated learning (FL) paradigm, the original data are stored in independent clients while masked data are sent to a central server to be aggregated, which proposes a novel design approach to numerous domains.Given the wide application of recommendation systems (RS) in diverse domains, combining RS with FL techniques has been gaining momentum to reduce the computational cost, do cross-domain recommendation and protect users’ privacy while maintaining recommendations performance as traditional RS.The federated learning-based recommendation systems in recent years were comprehensively summarized.The difference between traditional and federated recommendation systems was analyzed, and the main research direction and progress of federated recommendation systems were demonstrated with comparison and analysis.Firstly, the traditional recommendation systems and their bottleneck were summarized.Then the federated learning paradigm was introduced.Furthermore, the advantages of combining federated learning with recommendation systems were depicted in two aspects: privacy protection and usage of multi-domain user information, along with the technical challenges during the combination.At the same time, the existing deployment of federated recommendation systems was illustrated in detail.Finally, future research on federated recommendation systems was prospected and summarized.

    Neighborhood conditional mutual information entropy attribute reduction algorithm for hybrid data
    Haibo LAN
    2022, 8(4):  133-144.  doi:10.11959/j.issn.2096-0271.2022066
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    Attribute reduction is an important research content of the rough set theory.Its main purpose is to eliminate irrelevant attributes in information systems, reduce data dimensions and improve data knowledge discovery performance.However, most of the attribute reduction methods based on a rough set do not consider the dependence between attributes, which makes the final attribute reduction result have some redundant attributes.An attribute reduction algorithm based on neighborhood conditional mutual information entropy was proposed.Firstly, based on the traditional neighborhood entropy, a hybrid neighborhood mutual information entropy model and a hybrid neighborhood conditional mutual information entropy model were proposed for hybrid data.Then, the two entropy models were used to evaluate the attribute dependence and attribute heuristic search of the hybrid information system, and an attribute reduction algorithm was designed.Finally, through the experimental analysis of UCI data sets, it was proved that the algorithm had higher attribute reduction performance.

    APPLICATION
    Exploration and practice of trusted AI governance framework
    Zhengxun XIA, Jianfei TANG, Shengmei LUO, Yan ZHANG
    2022, 8(4):  145-164.  doi:10.11959/j.issn.2096-0271.2022036
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    Artificial intelligence (AI) has further improved the automation of information systems, however, some issues have been exposed during its large-scale application, such as data security, privacy protection, and fair ethics.To solve these issues and promote the transition of AI from available systems to trusted systems, the T-DACM trusted AI governance framework was proposed to improve the credibility of AI from the four levels of data, algorithm, calculation, and management.Different components were designed to solve specific issues such as data security, model security, privacy protection, model black box, fairness, accountability, and traceability.T-DACM practice case provides a demonstration of the trusted AI governance framework for the industry and provides a certain reference for subsequent product research and development based on the trusted AI governance framework.

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