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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2024,7,31]]},"abstract":"<jats:p>The Metaverse, envisioned as the next-generation Internet, will be constructed via twining a practical world in a virtual form, wherein Meterverse service providers (MSPs) are required to collect massive data from Meterverse users (MUs). In this regard, a critical demand exists for MSPs to motivate MUs to contribute computing resources and data while preserving user privacy. Federated learning (FL), as a privacy-preserving collaborative machine learning paradigm, can support distributed intensive computation in the Metaverse. In this work, we first investigate minting the machine learning models into NFT with FL assistance (referred to as FL-NFT), such that MUs as stakeholders can control the ownership and share the economic value of user-generated content (UGC). Specifically, MUs are encouraged to establish a decentralized autonomous organization (i.e., MU-DAO) to aggregate local models and mint FL-NFT. MUs and MSPs optimize the strategies by formulating an imperfect information Stackelberg game to trade off the cost and benefit. We apply the backward induction to derive the equilibrium solution. Then, we construct a privacy-preserving multi-winner sealed-bid auction mechanism (PMS-AM), in which the Hidden Markov Model assists MSPs in choosing rational bidding strategies according to historical bids, and the double auction mechanism determines the winners and price of FL-NFT. Finally, the numerical results based on theoretical analysis and simulations demonstrate that the proposed PMS-AM can increase the quality of FL-NFT and achieve the economic properties of incentive mechanisms such as individual rationality and incentive compatibility.<\/jats:p>","DOI":"10.1145\/3599971","type":"journal-article","created":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T11:40:51Z","timestamp":1686138051000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["A Privacy-preserving Auction Mechanism for Learning Model as an NFT in Blockchain-driven Metaverse"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9220-2694","authenticated-orcid":false,"given":"Qinnan","family":"Zhang","sequence":"first","affiliation":[{"name":"Central University of Finance and Economics, Beijing, China and Singapore University of Technology and Design, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4440-941X","authenticated-orcid":false,"given":"Zehui","family":"Xiong","sequence":"additional","affiliation":[{"name":"Singapore University of Technology and Design, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4923-9939","authenticated-orcid":false,"given":"Jianming","family":"Zhu","sequence":"additional","affiliation":[{"name":"Central University of Finance and Economics, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8118-411X","authenticated-orcid":false,"given":"Sheng","family":"Gao","sequence":"additional","affiliation":[{"name":"Central University of Finance and Economics, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6918-3701","authenticated-orcid":false,"given":"Wanting","family":"Yang","sequence":"additional","affiliation":[{"name":"Jilin University, China and Singapore University of Technology and Design, Singapore, Singapore"}]}],"member":"320","published-online":{"date-parts":[[2024,3,27]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aaa1465"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSPCS.2012.6507972"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2019.2934027"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICNC47757.2020.9049708"},{"key":"e_1_3_2_6_2","unstructured":"Jianmin Chen Xinghao Pan Rajat Monga Samy Bengio and Rafal Jozefowicz. 2016. 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