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Secure Networked Inference with Unreliable Data Sources

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Abstract

This chapter introduces the mathematical background of conventional inference by describing the typical inference problems of detection, estimation, classification, and tracking. Specific challenges associated with inference in practical sensor networks are discussed next, with emphasis on Byzantines. Finally, a taxonomy of results is presented: the results are divided into fundamental limits of attack strategies from the Byzantine’s perspective and mitigation strategies from the network’s perspective.

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Notes

  1. 1.

    Strictly speaking, it is the probability density function (pdf), but we use the term distribution and pdf interchangeably.

  2. 2.

    We are focusing on the continuous case. Discrete cases can be considered in a similar manner.

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Authors and Affiliations

  1. IBM Research—Thomas J. Watson Research, Yorktown Heights, NY, USA

    Aditya Vempaty

  2. Department of Computing Applications and Research , Lawrence Livermore National Laboratory, Livermore, CA, USA

    Bhavya Kailkhura

  3. Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, USA

    Pramod K. Varshney

Authors
  1. Aditya Vempaty
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  2. Bhavya Kailkhura
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  3. Pramod K. Varshney
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Correspondence to Aditya Vempaty .

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Vempaty, A., Kailkhura, B., Varshney, P.K. (2018). Background. In: Secure Networked Inference with Unreliable Data Sources. Springer, Singapore. https://doi.org/10.1007/978-981-13-2312-6_2

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  • DOI: https://doi.org/10.1007/978-981-13-2312-6_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2311-9

  • Online ISBN: 978-981-13-2312-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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