Anonymous anti-Sybil attack protocol for mobile healthcare networks analytics

Peng Zhang, Xiafei Zhang, Xiaoqiang Sun, Joseph K. Liu, Jianping Yu, Zoe L. Jiang

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

    Abstract

    Mobile Healthcare Networks (MHN) continuouslycollect the patients' health data sensed by wearable devices, andanalyze the collected data pre-processed by servers combinedwith medical histories, such that disease diagnosis and treatmentare improved, and the heavy burden on the existing healthservices is released. However, the network is vulnerable to Sybilattacks, which would degrade network performance, disruptproceedings, manipulate data or cheat others maliciously. What'smore, the user is reluctant to leak identity privacy, so the identityprivacy preserving makes Sybil defenses more difficult. One ofthe best choices is mutually authenticating each other with noidentity information involved. Thus, we propose a fine-grainedauthentication scheme based on Attribute-Based Signature (ABS)using lattice assumption, where a signer is authorized by an at-tribute set instead of single identity string. This ABS scheme usesFiat-Shamir framework and supports flexible threshold signaturepredicates. Moreover, to anonymously guarantee integrity andavailability of health data in MHN, we design an anonymousanti-Sybil attack protocol based on our ABS scheme, so thatSybil attacks are prevented. As there is no linkability betweenidentities and services, the users' identity privacy is protected. Finally, we have analyzed the security and simulated the runningtime for our proposed ABS scheme.

    Original languageEnglish
    Title of host publicationProceedings - The 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, The 11th IEEE International Conference on Big Data Science and Engineering and the 14th IEEE International Conference on Embedded Software and Systems
    Subtitle of host publication2017 IEEE Trustcom/BigDataSE/ICESS
    EditorsPriyadarsi Nanda, Yang Xiang, Yi Mu
    Place of PublicationPiscataway NJ USA
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages668-674
    Number of pages7
    ISBN (Print)9781509049059
    DOIs
    Publication statusPublished - 7 Sep 2017
    EventIEEE International Conference on Trust, Security and Privacy in Computing and Communications, IEEE International Conference on Big Data Science and Engineering and International Conference on Embedded Software and Systems (Trustcom/BigDataSE/ICESS) 2017 - Novotel Sydney Central Hotel, Sydney, Australia
    Duration: 1 Aug 20174 Aug 2017
    Conference number: 16th
    https://web.archive.org/web/20170816014543/http://www.stprp-activity.com/TrustCom2017

    Conference

    ConferenceIEEE International Conference on Trust, Security and Privacy in Computing and Communications, IEEE International Conference on Big Data Science and Engineering and International Conference on Embedded Software and Systems (Trustcom/BigDataSE/ICESS) 2017
    Abbreviated titleTrustcom/BigDataSE/ICESS 2017
    CountryAustralia
    CitySydney
    Period1/08/174/08/17
    OtherAll 3 of these conferences are on the ERA2018 event list, have related it to the first listed conference

    16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017
    Internet address

    Cite this

    Zhang, P., Zhang, X., Sun, X., Liu, J. K., Yu, J., & Jiang, Z. L. (2017). Anonymous anti-Sybil attack protocol for mobile healthcare networks analytics. In P. Nanda, Y. Xiang, & Y. Mu (Eds.), Proceedings - The 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, The 11th IEEE International Conference on Big Data Science and Engineering and the 14th IEEE International Conference on Embedded Software and Systems: 2017 IEEE Trustcom/BigDataSE/ICESS (pp. 668-674). [8029501] Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.298
    Zhang, Peng ; Zhang, Xiafei ; Sun, Xiaoqiang ; Liu, Joseph K. ; Yu, Jianping ; Jiang, Zoe L. / Anonymous anti-Sybil attack protocol for mobile healthcare networks analytics. Proceedings - The 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, The 11th IEEE International Conference on Big Data Science and Engineering and the 14th IEEE International Conference on Embedded Software and Systems: 2017 IEEE Trustcom/BigDataSE/ICESS. editor / Priyadarsi Nanda ; Yang Xiang ; Yi Mu. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2017. pp. 668-674
    @inproceedings{8ef504697daf4b0796747ed0bb01cd35,
    title = "Anonymous anti-Sybil attack protocol for mobile healthcare networks analytics",
    abstract = "Mobile Healthcare Networks (MHN) continuouslycollect the patients' health data sensed by wearable devices, andanalyze the collected data pre-processed by servers combinedwith medical histories, such that disease diagnosis and treatmentare improved, and the heavy burden on the existing healthservices is released. However, the network is vulnerable to Sybilattacks, which would degrade network performance, disruptproceedings, manipulate data or cheat others maliciously. What'smore, the user is reluctant to leak identity privacy, so the identityprivacy preserving makes Sybil defenses more difficult. One ofthe best choices is mutually authenticating each other with noidentity information involved. Thus, we propose a fine-grainedauthentication scheme based on Attribute-Based Signature (ABS)using lattice assumption, where a signer is authorized by an at-tribute set instead of single identity string. This ABS scheme usesFiat-Shamir framework and supports flexible threshold signaturepredicates. Moreover, to anonymously guarantee integrity andavailability of health data in MHN, we design an anonymousanti-Sybil attack protocol based on our ABS scheme, so thatSybil attacks are prevented. As there is no linkability betweenidentities and services, the users' identity privacy is protected. Finally, we have analyzed the security and simulated the runningtime for our proposed ABS scheme.",
    author = "Peng Zhang and Xiafei Zhang and Xiaoqiang Sun and Liu, {Joseph K.} and Jianping Yu and Jiang, {Zoe L.}",
    year = "2017",
    month = "9",
    day = "7",
    doi = "10.1109/Trustcom/BigDataSE/ICESS.2017.298",
    language = "English",
    isbn = "9781509049059",
    pages = "668--674",
    editor = "Priyadarsi Nanda and Yang Xiang and Yi Mu",
    booktitle = "Proceedings - The 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, The 11th IEEE International Conference on Big Data Science and Engineering and the 14th IEEE International Conference on Embedded Software and Systems",
    publisher = "IEEE, Institute of Electrical and Electronics Engineers",
    address = "United States of America",

    }

    Zhang, P, Zhang, X, Sun, X, Liu, JK, Yu, J & Jiang, ZL 2017, Anonymous anti-Sybil attack protocol for mobile healthcare networks analytics. in P Nanda, Y Xiang & Y Mu (eds), Proceedings - The 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, The 11th IEEE International Conference on Big Data Science and Engineering and the 14th IEEE International Conference on Embedded Software and Systems: 2017 IEEE Trustcom/BigDataSE/ICESS., 8029501, IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 668-674, IEEE International Conference on Trust, Security and Privacy in Computing and Communications, IEEE International Conference on Big Data Science and Engineering and International Conference on Embedded Software and Systems (Trustcom/BigDataSE/ICESS) 2017, Sydney, Australia, 1/08/17. https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.298

    Anonymous anti-Sybil attack protocol for mobile healthcare networks analytics. / Zhang, Peng; Zhang, Xiafei; Sun, Xiaoqiang; Liu, Joseph K.; Yu, Jianping; Jiang, Zoe L.

    Proceedings - The 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, The 11th IEEE International Conference on Big Data Science and Engineering and the 14th IEEE International Conference on Embedded Software and Systems: 2017 IEEE Trustcom/BigDataSE/ICESS. ed. / Priyadarsi Nanda; Yang Xiang; Yi Mu. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 668-674 8029501.

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

    TY - GEN

    T1 - Anonymous anti-Sybil attack protocol for mobile healthcare networks analytics

    AU - Zhang, Peng

    AU - Zhang, Xiafei

    AU - Sun, Xiaoqiang

    AU - Liu, Joseph K.

    AU - Yu, Jianping

    AU - Jiang, Zoe L.

    PY - 2017/9/7

    Y1 - 2017/9/7

    N2 - Mobile Healthcare Networks (MHN) continuouslycollect the patients' health data sensed by wearable devices, andanalyze the collected data pre-processed by servers combinedwith medical histories, such that disease diagnosis and treatmentare improved, and the heavy burden on the existing healthservices is released. However, the network is vulnerable to Sybilattacks, which would degrade network performance, disruptproceedings, manipulate data or cheat others maliciously. What'smore, the user is reluctant to leak identity privacy, so the identityprivacy preserving makes Sybil defenses more difficult. One ofthe best choices is mutually authenticating each other with noidentity information involved. Thus, we propose a fine-grainedauthentication scheme based on Attribute-Based Signature (ABS)using lattice assumption, where a signer is authorized by an at-tribute set instead of single identity string. This ABS scheme usesFiat-Shamir framework and supports flexible threshold signaturepredicates. Moreover, to anonymously guarantee integrity andavailability of health data in MHN, we design an anonymousanti-Sybil attack protocol based on our ABS scheme, so thatSybil attacks are prevented. As there is no linkability betweenidentities and services, the users' identity privacy is protected. Finally, we have analyzed the security and simulated the runningtime for our proposed ABS scheme.

    AB - Mobile Healthcare Networks (MHN) continuouslycollect the patients' health data sensed by wearable devices, andanalyze the collected data pre-processed by servers combinedwith medical histories, such that disease diagnosis and treatmentare improved, and the heavy burden on the existing healthservices is released. However, the network is vulnerable to Sybilattacks, which would degrade network performance, disruptproceedings, manipulate data or cheat others maliciously. What'smore, the user is reluctant to leak identity privacy, so the identityprivacy preserving makes Sybil defenses more difficult. One ofthe best choices is mutually authenticating each other with noidentity information involved. Thus, we propose a fine-grainedauthentication scheme based on Attribute-Based Signature (ABS)using lattice assumption, where a signer is authorized by an at-tribute set instead of single identity string. This ABS scheme usesFiat-Shamir framework and supports flexible threshold signaturepredicates. Moreover, to anonymously guarantee integrity andavailability of health data in MHN, we design an anonymousanti-Sybil attack protocol based on our ABS scheme, so thatSybil attacks are prevented. As there is no linkability betweenidentities and services, the users' identity privacy is protected. Finally, we have analyzed the security and simulated the runningtime for our proposed ABS scheme.

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    U2 - 10.1109/Trustcom/BigDataSE/ICESS.2017.298

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    PB - IEEE, Institute of Electrical and Electronics Engineers

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    Zhang P, Zhang X, Sun X, Liu JK, Yu J, Jiang ZL. Anonymous anti-Sybil attack protocol for mobile healthcare networks analytics. In Nanda P, Xiang Y, Mu Y, editors, Proceedings - The 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, The 11th IEEE International Conference on Big Data Science and Engineering and the 14th IEEE International Conference on Embedded Software and Systems: 2017 IEEE Trustcom/BigDataSE/ICESS. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2017. p. 668-674. 8029501 https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.298