Blockchain-Based Privacy-Preserving and Accountable Mobile Edge Outsourcing Computing Framework for the Metaverse

Mobile Edge Computing (MEC) enables Metaverse Terminal Devices (MTD) to perform complex tasks, including graphic rendering and physical simulation, by leveraging low-latency outsourced computing. However, existing research has not fully addressed the challenge of establishing an efficient outsourced...

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Veröffentlicht in:IEEE transactions on green communications and networking 2024-08, p.1-1
Hauptverfasser: Li, Chunpei, Liu, Chen, Liu, Peng, Li, Xianxian, Qiu, Wangjie, Lei, Lei, Jin, Yanli
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Sprache:eng
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Zusammenfassung:Mobile Edge Computing (MEC) enables Metaverse Terminal Devices (MTD) to perform complex tasks, including graphic rendering and physical simulation, by leveraging low-latency outsourced computing. However, existing research has not fully addressed the challenge of establishing an efficient outsourced computing service within an open and dynamic MEC environment that simultaneously ensures privacy and accountability. To address this, we proposes a blockchain-based privacy-preserving and accountable mobile edge outsourcing computing framework for the Metaverse, termed Meta-BMEOC. Specifically, we have designed an outsourcing computing protocol based on smart contracts and threshold secret sharing, enabling MTD to outsource tasks to multiple edge servers while preserving privacy. Furthermore, we have developed an off-chain smart contract protocol based on a Trusted Execution Environment. This protocol is designed to reduce the risk of malicious edge servers colluding to reconstruct the computational tasks of MTD, and it enables accountability for servers that return erroneous results. Additionally, we designed an incentive mechanism to resist malicious attacks and ensure system security and stability. Security analysis and experimental evaluation show that Meta-BMEOC not only ensures the privacy and accountability of outsourced computing but also provides outsourced computing services with lower computational latency.
ISSN:2473-2400
2473-2400
DOI:10.1109/TGCN.2024.3451513