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Efficient and robust data aggregation using untrusted infrastructure

Published: 26 November 2013 Publication History
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  • Abstract

    We present two protocols for data aggregation in networks consisting of many subsystems run by different and potentially adversarial parties. In such a case the messages from the nodes of a subnetwork are aggregated and transmitted to the sink over intermediate nodes which are not controlled by the subnetwork, and which potentially are influenced by an adversary. The adversary aims at changing the result of computations and/or learning the data processed by the stations of the subnetwork.
    We develop and formally analyze two protocols that enable to detect adversarial manipulations without sacrificing efficiency of the data aggregation. Our scheme is not protected from adversary attaining to destroy communication. However, any manipulation is detected w.h.p. One of the protocols supports multi-route processing without double counting effects, tolerates failures of intermediate nodes and lack of synchronization. The second one is very efficient in terms of communication volume and computational complexity. Both protocols provide very high level of data confidentiality and protection against different kinds of threats.

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    Cited By

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    • (2015)Some Remarks and Ideas About Monetization of Sensitive DataRevised Selected Papers of the 10th International Workshop on Data Privacy Management, and Security Assurance - Volume 948110.1007/978-3-319-29883-2_8(118-133)Online publication date: 21-Sep-2015

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    cover image ACM Other conferences
    SIN '13: Proceedings of the 6th International Conference on Security of Information and Networks
    November 2013
    483 pages
    ISBN:9781450324984
    DOI:10.1145/2523514
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Sponsors

    • Macquarie U., Austarlia
    • MNIT: Malaviya National Institute of Technology
    • Aksaray Univ.: Aksaray University
    • SFedU: Southern Federal University

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 26 November 2013

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    • Aksaray Univ.
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    • (2015)Some Remarks and Ideas About Monetization of Sensitive DataRevised Selected Papers of the 10th International Workshop on Data Privacy Management, and Security Assurance - Volume 948110.1007/978-3-319-29883-2_8(118-133)Online publication date: 21-Sep-2015

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