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Consumer-Friendly Methods for Privacy Protection Against Cleaning Robots

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Attacks and Defenses for the Internet-of-Things (ADIoT 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13745))

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Abstract

As Internet of Things (IoT) technologies enter the consumer market, smart cleaning robots have gained high attention and usage in households. However, as the “privacy paradox” phenomenon states, consumers behave differently even if many claim to be concerned about smart robot privacy issues. In this paper, we describe our attempt to discover effective measures for average consumers to guard against potential privacy intrusions by cleaning robots. We define our target devices, provide an ideal smart home network topology and establish our threat model. We document network redirection and analytic methods we used during our research. We categorize existing privacy protection methods and describe their general procedures. We assess and evaluate the protection methods with regard to three aspects: protection effectiveness, functionality loss and consumer-friendliness. In the end we perform a tabular qualitative comparison and develop our vision for privacy protection against cleaning robots.

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Notes

  1. 1.

    This exact device model is no longer officially listed. The current variant “Dreame D9 Mistral Pro” [16] only differs in colour.

  2. 2.

    No substantial functional differences exist in the currently sold version 2.

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Author information

Authors and Affiliations

  1. Karlsruhe Institute of Technology, Karlsruhe, Germany

    Yanxiu Wuwang & Gunther Schiefer

Authors
  1. Yanxiu Wuwang
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  2. Gunther Schiefer
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Corresponding author

Correspondence to Yanxiu Wuwang .

Editor information

Editors and Affiliations

  1. Hong Kong Polytechnic University, Hong Kong, China

    Wenjuan Li

  2. University of Nottingham, Nottingham, UK

    Steven Furnell

  3. Technical University of Denmark, Kongens Lyngby, Denmark

    Weizhi Meng

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Wuwang, Y., Schiefer, G. (2022). Consumer-Friendly Methods for Privacy Protection Against Cleaning Robots. In: Li, W., Furnell, S., Meng, W. (eds) Attacks and Defenses for the Internet-of-Things. ADIoT 2022. Lecture Notes in Computer Science, vol 13745. Springer, Cham. https://doi.org/10.1007/978-3-031-21311-3_5

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  • DOI: https://doi.org/10.1007/978-3-031-21311-3_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21310-6

  • Online ISBN: 978-3-031-21311-3

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