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Privacy protected data forwarding in human associated delay tolerant networks

conference contribution
posted on 2013-01-01, 00:00 authored by Longxiang GaoLongxiang Gao, Ming Li, Wanlei Zhou, W Shi
Human associated delay-tolerant networks (HDTNs) are new networks for DTNs, where mobile devices are associated with humans and demonstrate social related communication characteristics. As most of recent works use real social trace files to study the date forwarding in HDTNs, the privacy protection becomes a serious issue. Traditional privacy protections need to keep the attributes semantics, such as data mining and information retrieval. However, in HDTNs, it is not necessary to keep these meaningful semantics. In this paper, instead, we propose to anonymize the original data by coding to preserve individual's privacy and apply Privacy Protected Data Forwarding (PPDF) model to select the top N nodes to perform the multicast. We use both MIT Reality and Infocom 06 datasets, which are human associated mobile network trace file, to simulate our model. The results of our simulations show that this method can achieve a high data forwarding performance while protect the nodes' privacy as well.

History

Event

IEEE Trust, Security and Privacy in Computing and Communications. Conference (12th : 2013 : Melbourne, Vic)

Pagination

586 - 593

Publisher

IEEE Computer Society

Location

Melbourne, Vic.

Place of publication

Piscataway, N.J.

Start date

2013-07-16

End date

2013-07-18

ISBN-13

9780769550220

Language

eng

Publication classification

E1 Full written paper - refereed; E Conference publication

Copyright notice

2013, IEEE

Title of proceedings

TrustCom 2013 : Proceedings of the 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications

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