Learning-Based Privacy-Preserving Computation Offloading in Multi-Access Edge Computing

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

Learning-Based Privacy-Preserving Computation Offloading in Multi-Access Edge Computing

You, F Yuan, X

Ni, W Jamalipour, A

Permalink

Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: GLOBECOM 2023 - 2023 IEEE Global Communications Conference, 2024, 00, pp. 922-927
Issue Date:: 2024-02-26

Recently Added

	Filename	Description	Size
	1714590.pdf	Published version	1.1 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is new to OPUS and is not currently available.

Full metadata record

Field	Value	Language
dc.contributor.author	You, F
dc.contributor.author	Yuan, X https://orcid.org/0000-0002-9167-1613
dc.contributor.author	Ni, W
dc.contributor.author	Jamalipour, A
dc.date	2023-12-04
dc.date.accessioned	2024-08-07T05:01:52Z
dc.date.available	2024-08-07T05:01:52Z
dc.date.issued	2024-02-26
dc.identifier.citation	GLOBECOM 2023 - 2023 IEEE Global Communications Conference, 2024, 00, pp. 922-927
dc.identifier.isbn	979-8-3503-1091-7
dc.identifier.issn	1930-529X
dc.identifier.issn	2576-6813
dc.identifier.uri	http://hdl.handle.net/10453/180261
dc.description.abstract	As a technology intended to reduce cellular network congestion and enhance user service quality computation offloading in Multi access Edge Computing MEC networks highlights the crucial issue of privacy protection This paper proposes a novel solution to the computation offloading and privacy protection problem in the MEC network using a Multi agent Deep Deterministic Policy Gradient MADDPG framework Our approach utilizes game theory to encourage computation offloading by modeling the interaction between cloudlets Data Center Operator DCO and users as an auction game We formulate the resource allocation and privacy protection as an auction game with multiple bidders and incomplete information and then use MADDPG to find an optimal solution To ensure privacy protection we design a Local Differential Privacy LDP method in the MADDPG algorithm Theoretical analysis and simulation results demonstrate the effectiveness of our approach in satisfying differential privacy and converging to an equilibrium The proposed solution holds significant promise in addressing the computation offloading and privacy protection challenges in MEC networks
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	GLOBECOM 2023 - 2023 IEEE Global Communications Conference
dc.relation.ispartof	GLOBECOM 2023 - 2023 IEEE Global Communications Conference
dc.relation.isbasedon	10.1109/globecom54140.2023.10437967
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	Learning-Based Privacy-Preserving Computation Offloading in Multi-Access Edge Computing
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Kuala Lumpur, Malaysia
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	recently_added	*
dc.date.updated	2024-08-07T05:01:51Z
pubs.finish-date	2023-12-08
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2023-12-04
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

As a technology intended to reduce cellular network congestion and enhance user service quality computation offloading in Multi access Edge Computing MEC networks highlights the crucial issue of privacy protection This paper proposes a novel solution to the computation offloading and privacy protection problem in the MEC network using a Multi agent Deep Deterministic Policy Gradient MADDPG framework Our approach utilizes game theory to encourage computation offloading by modeling the interaction between cloudlets Data Center Operator DCO and users as an auction game We formulate the resource allocation and privacy protection as an auction game with multiple bidders and incomplete information and then use MADDPG to find an optimal solution To ensure privacy protection we design a Local Differential Privacy LDP method in the MADDPG algorithm Theoretical analysis and simulation results demonstrate the effectiveness of our approach in satisfying differential privacy and converging to an equilibrium The proposed solution holds significant promise in addressing the computation offloading and privacy protection challenges in MEC networks

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/180261