Privacy on the Edge: Customizable Privacy-Preserving Context Sharing in Hierarchical Edge Computing

Gu, B; Gao, L; Wang, X; Qu, Y; Jin, J; Yu, S

Privacy on the Edge: Customizable Privacy-Preserving Context Sharing in Hierarchical Edge Computing

Gu, B Gao, L Wang, X Qu, Y Jin, J Yu, S

Permalink

Publisher:: IEEE COMPUTER SOC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Network Science and Engineering, 2020, 7, (4), pp. 2298-2309
Issue Date:: 2020-10-01

Closed Access

	Filename	Description	Size
	08790751.pdf	Published version	1.57 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Gu, B
dc.contributor.author	Gao, L
dc.contributor.author	Wang, X
dc.contributor.author	Qu, Y
dc.contributor.author	Jin, J
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.date.accessioned	2021-03-28T21:19:24Z
dc.date.available	2021-03-28T21:19:24Z
dc.date.issued	2020-10-01
dc.identifier.citation	IEEE Transactions on Network Science and Engineering, 2020, 7, (4), pp. 2298-2309
dc.identifier.issn	2327-4697
dc.identifier.issn	2327-4697
dc.identifier.uri	http://hdl.handle.net/10453/147580
dc.description.abstract	© 2013 IEEE. The booming of edge computing enables and reshapes this big data era. However, privacy issues arise because increasing volume of data are published per second while the edge devices can only provide limited computing and storage resources. In addition, this has been aggravated by new emerging features of edge computing, such as decentralized and hierarchical infrastructure, mobility, and content-Aware applications. Although some existing privacy preserving methods are extended to this domain, the privacy issues of data dissemination between multiple edge nodes and end users is barely studied. Motivated by this, we propose a dynamic customizable privacy-preserving model based on Markov decision process to obtain the optimized trade-off between customizable privacy protection and data utility. We start with establishing a game model between users and adversaries based on a QoS-based payoff function. A modified reinforcement learning algorithm is deployed to derive the exclusive Nash Equilibrium. Furthermore, the model can achieve fast convergence by the reduction of cardinality from n to 2. Extensive experimental results confirm the significance of the proposed model comparing to the existing work both in terms of effectiveness and feasibility.
dc.language	English
dc.publisher	IEEE COMPUTER SOC
dc.relation.ispartof	IEEE Transactions on Network Science and Engineering
dc.relation.isbasedon	10.1109/TNSE.2019.2933639
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Privacy on the Edge: Customizable Privacy-Preserving Context Sharing in Hierarchical Edge Computing
dc.type	Journal Article
utslib.citation.volume	7
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2021-03-28T21:19:22Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	7
utslib.citation.issue	4

Abstract:

© 2013 IEEE. The booming of edge computing enables and reshapes this big data era. However, privacy issues arise because increasing volume of data are published per second while the edge devices can only provide limited computing and storage resources. In addition, this has been aggravated by new emerging features of edge computing, such as decentralized and hierarchical infrastructure, mobility, and content-Aware applications. Although some existing privacy preserving methods are extended to this domain, the privacy issues of data dissemination between multiple edge nodes and end users is barely studied. Motivated by this, we propose a dynamic customizable privacy-preserving model based on Markov decision process to obtain the optimized trade-off between customizable privacy protection and data utility. We start with establishing a game model between users and adversaries based on a QoS-based payoff function. A modified reinforcement learning algorithm is deployed to derive the exclusive Nash Equilibrium. Furthermore, the model can achieve fast convergence by the reduction of cardinality from n to 2. Extensive experimental results confirm the significance of the proposed model comparing to the existing work both in terms of effectiveness and feasibility.

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

http://hdl.handle.net/10453/147580