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International Conference on Communications and Networking in China

ChinaCom 2016: Communications and Networking pp 200–207Cite as

A Method for Countering Snooping-Based Side Channel Attacks in Smart Home Applications

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Abstract

In recent years, with the rapid development of the Internet of Things (IoT), the information technology has been widely used in smart home applications. On the other hand, smart home technology closely related to people’s privacy, which is not much considered by smart home vendors, making the privacy protection of smart home a hot research topic. Traditional encryption methods can ensure the security of the transmission process, but it can hardly resist the side channel attacks. Adversaries can analyze the radio frequency signals of wireless sensors and timestamp series to acquire the Activity of Daily Living (ADL). The most simple and efficient way to counter side channel attacks is to add noise into the transmitted data sequence. In this paper, we propose an improved method based on Logistic Regression (LR), which can be adapted to network status to protect the privacy of residents in smart home environments. Compared with other similar approaches, our method has the advantage of low energy consumption, low latency, strong adaptability and good effect of privacy protection.

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References

  1. Theoharidou, M., Tsalis, N., Gritzalis, D.: Smart Home Solutions: Privacy Issues. Handbook of Smart Homes, Health Care and Well-Being, pp. 1–14. Springer, Cham (2014)

    Book  Google Scholar 

  2. Alami, A., Benhlima, L., Bah, S.: An overview of privacy preserving techniques in smart home wireless sensor networks. In: 10th International Conference on Intelligent Systems: Theories and Applications, pp. 1–4. IEEE Press, Rabat (2015)

    Google Scholar 

  3. Sanchez, I., Satta, R., Fovino, I.N., Baldini, G.: Privacy leakages in smart home wireless technologies. In: International Carnahan Conference on Security Technology, pp. 1–6. IEEE Press, Rome (2014)

    Google Scholar 

  4. Srinivasan, V., Stankovic, J., Whitehouse, K.: Protecting your daily in-home activity information from a wireless snooping attack. In: International Conference on Ubiquitous Computing, pp. 202–211. ACM Press, Seoul (2008)

    Google Scholar 

  5. Park, H., Park, T., Sang, H.S.: A comparative study of privacy protection methods for smart home environments. Int. J. Smart Home 7, 1–12 (2013)

    Google Scholar 

  6. Park, H., Basaran, C., Park, T., Son, S.H.: Energy-efficient privacy protection for smart home environments using behavioral semantics. J. Sens. 14, 16235–16257 (2014)

    Article  Google Scholar 

  7. Yang, Y., Shao, M., Zhu, S., Cao, G.: Towards statistically strong source anonymity for sensor networks. In: 27th Conference on Computer Communications. pp. 51–55. IEEE INFOCOM, Phoenix (2008)

    Google Scholar 

  8. Van Kasteren, T., Noulas, A., Englebienne, G.: Accurate activity recognition in a home setting. In: International Conference on Ubiquitous Computing, pp. 1–9. ACM Press, Seoul (2008)

    Google Scholar 

  9. Song, Y., Cai, Q., Nie, F., Zhang, C.: Semi-supervised additive logistic regression: a gradient descent solution. J. Tsinghua Sci. Technol. 12, 638–646 (2007)

    Article  MathSciNet  Google Scholar 

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Acknowledgement

The work in this paper has been supported by Beijing Natural Science Foundation (4142008), National Nature Science Foundation of China (61272500) and National High-tech R&D Program (863 Program) (2015AA017204).

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Authors and Affiliations

  1. School of Software Engineering, Beijing Engineering Research Center for IoT Software and Systems, Beijing University of Technology, Beijing, 100124, China

    Jingsha He, Qi Xiao & Muhammad Salman Pathan

Authors
  1. Jingsha He
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  2. Qi Xiao
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  3. Muhammad Salman Pathan
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Corresponding author

Correspondence to Qi Xiao .

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Editors and Affiliations

  1. Post and Telecommunications, Chongqing University, Chongqing, China

    Qianbin Chen

  2. Harbin Institute of Technology (HIT), Harbin, China

    Weixiao Meng

  3. Xidian University, Xi’an, China

    Liqiang Zhao

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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He, J., Xiao, Q., Pathan, M.S. (2018). A Method for Countering Snooping-Based Side Channel Attacks in Smart Home Applications. In: Chen, Q., Meng, W., Zhao, L. (eds) Communications and Networking. ChinaCom 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 209. Springer, Cham. https://doi.org/10.1007/978-3-319-66625-9_20

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  • DOI: https://doi.org/10.1007/978-3-319-66625-9_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66624-2

  • Online ISBN: 978-3-319-66625-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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