AMDD : Exploring entropy based anonymous multi-dimensional data detection for network optimization in human associated DTNs

Gao, Longxiang, Li, Ming, Zhu, Tianqing, Bonti, Alessio, Zhou, Wanlei and Yu, Shui 2012, AMDD : Exploring entropy based anonymous multi-dimensional data detection for network optimization in human associated DTNs, in TrustCom 2012 : Proceedings of the 11th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, IEEE, Piscataway, N.J., pp. 1245-1250.

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Title AMDD : Exploring entropy based anonymous multi-dimensional data detection for network optimization in human associated DTNs
Author(s) Gao, Longxiang
Li, Ming
Zhu, Tianqing
Bonti, Alessio
Zhou, Wanlei
Yu, Shui
Conference name IEEE Trust, Security and Privacy in Computing and Communications. Conference (11th : 2012 : Liverpool, England)
Conference location Liverpool, England
Conference dates 25-27 Jun. 2012
Title of proceedings TrustCom 2012 : Proceedings of the 11th IEEE International Conference on Trust, Security and Privacy in Computing and Communications
Editor(s) Min, Geyong
Wu, Yulei
Lei, Liu (Chris)
Jin, Xiaolong
Jarvis, Stephen
Al-Dubai, Ahmed Y.
Publication date 2012
Conference series IEEE Trust, Security and Privacy in Computing and Communications Conference
Start page 1245
End page 1250
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) delay-tolerant network
entropy
privacy
Summary Human associated delay-tolerant networks (HDTNs) are new networks where mobile devices are associated with humans and demonstrate social-related communication characteristics. Most of recent works use real social trace file to analyse its social characteristics, however social-related data is sensitive and has concern of privacy issues. In this paper, we propose an anonymous method that anonymize the original data by coding to preserve individual's privacy. The Shannon entropy is applied to the anonymous data to keep rich useful social characteristics for network optimization, e.g. routing optimization. We use an existing MIT reality dataset and Infocom 06 dataset, which are human associated mobile network trace files, to simulate our method. The results of our simulations show that this method can make data anonymously while achieving network optimization.
ISBN 9780769547459
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30051369

Document type: Conference Paper
Collection: School of Information Technology
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