Review of scalable privacy protection techniques in mobile crowdsensing service for security of data

Su, J; Konda, S; Momani, M

Review of scalable privacy protection techniques in mobile crowdsensing service for security of data

Su, J Konda, S Momani, M

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA), 2021, 00, pp. 1-9
Issue Date:: 2021-03-09

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Field	Value	Language
dc.contributor.author	Su, J
dc.contributor.author	Konda, S
dc.contributor.author	Momani, M
dc.date	2020-11-25
dc.date.accessioned	2022-06-28T05:02:57Z
dc.date.available	2022-06-28T05:02:57Z
dc.date.issued	2021-03-09
dc.identifier.citation	2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA), 2021, 00, pp. 1-9
dc.identifier.isbn	978-1-7281-9437-0
dc.identifier.uri	http://hdl.handle.net/10453/158430
dc.description.abstract	Mobile crowdsensing is a service based on a group of different individuals that have a device. The MCS (Mobile crowdsensing) is used for communication and transferring of data. It is capable of sensing and computing such data that are based on some information such as measuring, mapping, analyzing, and estimating. It can be used for effective decision-making in-crowd. The data generated in by crowd is used for task generation, and the task is assigned to different users and requesters. Due to numerous jobs, there can be a situation of task similarity generates, which may affect the privacy of users or workers in crowdsensing. The problem of privacy can be solved with the help of privacy protection techniques in crowdsensing. This work aims to propose a system based MCS technique for Privacy protection of data with proper scalability. CPP (Crowdsensing Privacy Protection) taxonomy is used that is based on the comprehensiveness and fitness of good. The usefulness of the proposed arrangement is explained by ordering 30 state-of-the-art solutions. Improved consequences are based on extraordinary assets and diminish of different MCS privacy protection techniques. It can be concluded that by employing the MCS privacy protection system for securing user data based on detection and learning algorithms with accurate dimensions. This research investigates the current innovations and techniques in the field of MCS for scalable privacy protection. Different relevant algorithms are used for effective decision making for users and requestors of MCS.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)
dc.relation.ispartof	International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications
dc.relation.isbasedon	10.1109/citisia50690.2020.9371826
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Review of scalable privacy protection techniques in mobile crowdsensing service for security of data
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Sydney, Australia
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	*
pubs.consider-herdc	false
dc.date.updated	2022-06-28T05:02:56Z
pubs.finish-date	2020-11-27
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2020-11-25
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

Mobile crowdsensing is a service based on a group of different individuals that have a device. The MCS (Mobile crowdsensing) is used for communication and transferring of data. It is capable of sensing and computing such data that are based on some information such as measuring, mapping, analyzing, and estimating. It can be used for effective decision-making in-crowd. The data generated in by crowd is used for task generation, and the task is assigned to different users and requesters. Due to numerous jobs, there can be a situation of task similarity generates, which may affect the privacy of users or workers in crowdsensing. The problem of privacy can be solved with the help of privacy protection techniques in crowdsensing. This work aims to propose a system based MCS technique for Privacy protection of data with proper scalability. CPP (Crowdsensing Privacy Protection) taxonomy is used that is based on the comprehensiveness and fitness of good. The usefulness of the proposed arrangement is explained by ordering 30 state-of-the-art solutions. Improved consequences are based on extraordinary assets and diminish of different MCS privacy protection techniques. It can be concluded that by employing the MCS privacy protection system for securing user data based on detection and learning algorithms with accurate dimensions. This research investigates the current innovations and techniques in the field of MCS for scalable privacy protection. Different relevant algorithms are used for effective decision making for users and requestors of MCS.

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

http://hdl.handle.net/10453/158430