A two-stage locality-sensitive hashing based approach for privacy-preserving mobile service recommendation in cross-platform edge environment

Qi, L; Zhang, X; Dou, W; Hu, C; Yang, C; Chen, J

A two-stage locality-sensitive hashing based approach for privacy-preserving mobile service recommendation in cross-platform edge environment

Qi, L Zhang, X Dou, W Hu, C Yang, C Chen, J

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Publication Type:: Journal Article
Citation:: Future Generation Computer Systems, 2018, 88 pp. 636 - 643
Issue Date:: 2018-11-01

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Field	Value	Language
dc.contributor.author	Qi, L	en_US
dc.contributor.author	Zhang, X	en_US
dc.contributor.author	Dou, W	en_US
dc.contributor.author	Hu, C	en_US
dc.contributor.author	Yang, C	en_US
dc.contributor.author	Chen, J	en_US
dc.date.issued	2018-11-01	en_US
dc.identifier.citation	Future Generation Computer Systems, 2018, 88 pp. 636 - 643	en_US
dc.identifier.issn	0167-739X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/129556
dc.description.abstract	© 2018 Elsevier B.V. With the increasing popularity of service computing paradigm, tremendous resources or services are emerging rapidly on the Web, imposing heavy burdens on the service selection decisions of users. In this situation, recommendation (e.g., collaborative filtering) has been considered as one of the most effective ways to alleviate such burdens. However, in the mobile and edge environment, the service recommendation bases, i.e., historical service usage data are often generated from various mobile devices (e.g., Smartphone and PDA) and stored in different edge platforms. Therefore, effective collaboration between these distributed edge platforms plays an important role in the successful mobile service recommendation. Such a cross-platform collaboration process often faces the following two challenges. First, a platform is often reluctant to release its data to other platforms due to privacy concerns. Second, the collaboration efficiency is often low when the data in each platform update frequently. In view of these two challenges, we introduce MinHash, an instance of Locality-Sensitive Hashing (LSH), into service recommendation, and further put forward a novel privacy-preserving and scalable mobile service recommendation approach based on two-stage LSH, named SerRectwo-LSH. Finally, extensive experiments are conducted on WS-DREAM, a real distributed service quality dataset, and the evaluation results demonstrate that both the service recommendation accuracy and the scalability have been significantly improved while privacy preservation is guaranteed.	en_US
dc.relation.ispartof	Future Generation Computer Systems	en_US
dc.relation.isbasedon	10.1016/j.future.2018.02.050	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Distributed Computing	en_US
dc.title	A two-stage locality-sensitive hashing based approach for privacy-preserving mobile service recommendation in cross-platform edge environment	en_US
dc.type	Journal Article
utslib.citation.volume	88	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0803 Computer Software	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	88	en_US

Abstract:

© 2018 Elsevier B.V. With the increasing popularity of service computing paradigm, tremendous resources or services are emerging rapidly on the Web, imposing heavy burdens on the service selection decisions of users. In this situation, recommendation (e.g., collaborative filtering) has been considered as one of the most effective ways to alleviate such burdens. However, in the mobile and edge environment, the service recommendation bases, i.e., historical service usage data are often generated from various mobile devices (e.g., Smartphone and PDA) and stored in different edge platforms. Therefore, effective collaboration between these distributed edge platforms plays an important role in the successful mobile service recommendation. Such a cross-platform collaboration process often faces the following two challenges. First, a platform is often reluctant to release its data to other platforms due to privacy concerns. Second, the collaboration efficiency is often low when the data in each platform update frequently. In view of these two challenges, we introduce MinHash, an instance of Locality-Sensitive Hashing (LSH), into service recommendation, and further put forward a novel privacy-preserving and scalable mobile service recommendation approach based on two-stage LSH, named SerRectwo-LSH. Finally, extensive experiments are conducted on WS-DREAM, a real distributed service quality dataset, and the evaluation results demonstrate that both the service recommendation accuracy and the scalability have been significantly improved while privacy preservation is guaranteed.

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

http://hdl.handle.net/10453/129556