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Article: Enhancing The Anonymity in Information Diffusion Based on Obfuscated Coded Data

TitleEnhancing The Anonymity in Information Diffusion Based on Obfuscated Coded Data
Authors
KeywordsAnonymity
Computer science
Correlation
Deterministic linear network coding
Encoding
Issue Date2019
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6488902
Citation
IEEE Transactions on Network Science and Engineering, 2019, v. 6 n. 4, p. 968-982 How to Cite?
AbstractLinear network coding (LNC) is a promising approach to facilitate anonymity in information diffusion because each packet is generated by linearly combining multiple incoming packets. Since the coefficients used in the linear combination would reveal the correlation between incoming and outgoing packets at a node, most existing studies on anonymous LNC design focus on encrypting these coefficients. Despite the importance of these studies, the correlation of coded content can still be analyzed and the potential of un-encrypted LNC has not been fully exploited. In this paper, we tackle these issues and we propose a novel ALNCode scheme that can enhance anonymity by generating outgoing packets that are correlated to incoming coded packets of multiple flows. With solid theoretical analysis, we first prove the probability that incoming coded packets from different flows are correlated. Then, we prove that, if such correlation exists, we can design deterministic LNC to obfuscate the correlation of packets. With the same condition, we also prove the probability that a randomly generated coded packet is correlated to coded packets in other flows. Besides the theoretical study, we conduct extensive numerical experiments to understand the impacts of various coding parameters and the performance of ALNCode in real scenarios.
Persistent Identifierhttp://hdl.handle.net/10722/273140
ISSN
2019 Impact Factor: 5.213
2015 SCImago Journal Rankings: 0.173
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, J-
dc.contributor.authorLu, K-
dc.contributor.authorWu, C-
dc.contributor.authorGu, N-
dc.contributor.authorWang, J-
dc.date.accessioned2019-08-06T09:23:17Z-
dc.date.available2019-08-06T09:23:17Z-
dc.date.issued2019-
dc.identifier.citationIEEE Transactions on Network Science and Engineering, 2019, v. 6 n. 4, p. 968-982-
dc.identifier.issn2327-4697-
dc.identifier.urihttp://hdl.handle.net/10722/273140-
dc.description.abstractLinear network coding (LNC) is a promising approach to facilitate anonymity in information diffusion because each packet is generated by linearly combining multiple incoming packets. Since the coefficients used in the linear combination would reveal the correlation between incoming and outgoing packets at a node, most existing studies on anonymous LNC design focus on encrypting these coefficients. Despite the importance of these studies, the correlation of coded content can still be analyzed and the potential of un-encrypted LNC has not been fully exploited. In this paper, we tackle these issues and we propose a novel ALNCode scheme that can enhance anonymity by generating outgoing packets that are correlated to incoming coded packets of multiple flows. With solid theoretical analysis, we first prove the probability that incoming coded packets from different flows are correlated. Then, we prove that, if such correlation exists, we can design deterministic LNC to obfuscate the correlation of packets. With the same condition, we also prove the probability that a randomly generated coded packet is correlated to coded packets in other flows. Besides the theoretical study, we conduct extensive numerical experiments to understand the impacts of various coding parameters and the performance of ALNCode in real scenarios.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6488902-
dc.relation.ispartofIEEE Transactions on Network Science and Engineering-
dc.rightsIEEE Transactions on Network Science and Engineering. Copyright © Institute of Electrical and Electronics Engineers.-
dc.rights©20xx 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.-
dc.subjectAnonymity-
dc.subjectComputer science-
dc.subjectCorrelation-
dc.subjectDeterministic linear network coding-
dc.subjectEncoding-
dc.titleEnhancing The Anonymity in Information Diffusion Based on Obfuscated Coded Data-
dc.typeArticle-
dc.identifier.emailWu, C: cwu@cs.hku.hk-
dc.identifier.authorityWu, C=rp01397-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TNSE.2018.2888848-
dc.identifier.scopuseid_2-s2.0-85059045466-
dc.identifier.scopuseid_2-s2.0-85059045466-
dc.identifier.hkuros299704-
dc.identifier.volume6-
dc.identifier.issue4-
dc.identifier.spage968-
dc.identifier.epage982-
dc.identifier.isiWOS:000502281600030-
dc.publisher.placeUnited States-
dc.identifier.issnl2327-4697-

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