A Novel Privacy-Preserving Incentive Mechanism for Multi-Access Edge Computing

You, F; Yuan, X; Ni, W; Jamalipour, A

A Novel Privacy-Preserving Incentive Mechanism for Multi-Access Edge Computing

You, F Yuan, X Ni, W Jamalipour, A

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Cognitive Communications and Networking, 2024, 10, (5), pp. 1928-1943
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	You, F
dc.contributor.author	Yuan, X
dc.contributor.author	Ni, W
dc.contributor.author	Jamalipour, A
dc.date.accessioned	2025-01-28T08:35:20Z
dc.date.available	2025-01-28T08:35:20Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Transactions on Cognitive Communications and Networking, 2024, 10, (5), pp. 1928-1943
dc.identifier.issn	2332-7731
dc.identifier.issn	2332-7731
dc.identifier.uri	http://hdl.handle.net/10453/184450
dc.description.abstract	Multi-access Edge Computing (MEC) has emerged as a promising solution for computation-intensive and latency-sensitive applications. Existing studies have often overlooked the critical aspect of users' privacy, hindering users from offloading their computation. This paper proposes a novel privacy-preserving mechanism for a two-level auction game aimed at incentivizing cloudlets and users to engage in computation offloading while safeguarding users' privacy. A many-to-many auction is designed between Data Center Operators (DCOs) and cloudlets to associate the cloudlets with the DCOs, where the perceivable privacy levels of users are parameterized as part of a DCO's utility. A many-to-one user-DCO auction is also designed, leveraging differential privacy (DP) to protect the users' private bid information. An exponential mechanism is developed, obfuscating intermediate reference prices disclosed during auctions by the DCOs, thereby safeguarding users' valuations, bid prices, and bidding behaviors. We prove that the proposed approach can guarantee DP, truthfulness, and equilibriums. Simulations demonstrate the superiority of the privacy-preserving two-layer auction game in reducing time delay and energy consumption while protecting the privacy of the users, surpassing the benchmark. The proposed mechanism effectively incentivizes computation offloading, making it a compelling choice for facilitating computation-intensive tasks.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Cognitive Communications and Networking
dc.relation.isbasedon	10.1109/TCCN.2024.3391303
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4604 Cybersecurity and privacy
dc.subject.classification	4606 Distributed computing and systems software
dc.title	A Novel Privacy-Preserving Incentive Mechanism for Multi-Access Edge Computing
dc.type	Journal Article
utslib.citation.volume	10
utslib.copyright.status	closed_access	*
dc.date.updated	2025-01-28T08:35:18Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	10
utslib.citation.issue	5

Abstract:

Multi-access Edge Computing (MEC) has emerged as a promising solution for computation-intensive and latency-sensitive applications. Existing studies have often overlooked the critical aspect of users' privacy, hindering users from offloading their computation. This paper proposes a novel privacy-preserving mechanism for a two-level auction game aimed at incentivizing cloudlets and users to engage in computation offloading while safeguarding users' privacy. A many-to-many auction is designed between Data Center Operators (DCOs) and cloudlets to associate the cloudlets with the DCOs, where the perceivable privacy levels of users are parameterized as part of a DCO's utility. A many-to-one user-DCO auction is also designed, leveraging differential privacy (DP) to protect the users' private bid information. An exponential mechanism is developed, obfuscating intermediate reference prices disclosed during auctions by the DCOs, thereby safeguarding users' valuations, bid prices, and bidding behaviors. We prove that the proposed approach can guarantee DP, truthfulness, and equilibriums. Simulations demonstrate the superiority of the privacy-preserving two-layer auction game in reducing time delay and energy consumption while protecting the privacy of the users, surpassing the benchmark. The proposed mechanism effectively incentivizes computation offloading, making it a compelling choice for facilitating computation-intensive tasks.

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http://hdl.handle.net/10453/184450