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An Adaptive Privacy Protection Method for Smart Home Environments Using Supervised Learning

Author

Listed:
  • Jingsha He

    (Faculty of Information Technology & Beijing Engineering Research Center for IoT Software and Systems, Beijing University of Technology, Beijing 100124, China
    College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China)

  • Qi Xiao

    (Faculty of Information Technology & Beijing Engineering Research Center for IoT Software and Systems, Beijing University of Technology, Beijing 100124, China)

  • Peng He

    (College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China)

  • Muhammad Salman Pathan

    (Faculty of Information Technology & Beijing Engineering Research Center for IoT Software and Systems, Beijing University of Technology, Beijing 100124, China)

Abstract

In recent years, smart home technologies have started to be widely used, bringing a great deal of convenience to people’s daily lives. At the same time, privacy issues have become particularly prominent. Traditional encryption methods can no longer meet the needs of privacy protection in smart home applications, since attacks can be launched even without the need for access to the cipher. Rather, attacks can be successfully realized through analyzing the frequency of radio signals, as well as the timestamp series, so that the daily activities of the residents in the smart home can be learnt. Such types of attacks can achieve a very high success rate, making them a great threat to users’ privacy. In this paper, we propose an adaptive method based on sample data analysis and supervised learning (SDASL), to hide the patterns of daily routines of residents that would adapt to dynamically changing network loads. Compared to some existing solutions, our proposed method exhibits advantages such as low energy consumption, low latency, strong adaptability, and effective privacy protection.

Suggested Citation

  • Jingsha He & Qi Xiao & Peng He & Muhammad Salman Pathan, 2017. "An Adaptive Privacy Protection Method for Smart Home Environments Using Supervised Learning," Future Internet, MDPI, vol. 9(1), pages 1-15, March.
    • Handle: RePEc:gam:jftint:v:9:y:2017:i:1:p:7-:d:92209
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    Citations

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    Cited by:

    1. Guoying Lin & Yuyao Yang & Feng Pan & Sijian Zhang & Fen Wang & Shuai Fan, 2019. "An Optimal Energy-Saving Strategy for Home Energy Management Systems with Bounded Customer Rationality," Future Internet, MDPI, vol. 11(4), pages 1-16, April.
    2. Georgios Kambourakis & Felix Gomez Marmol & Guojun Wang, 2018. "Security and Privacy in Wireless and Mobile Networks," Future Internet, MDPI, vol. 10(2), pages 1-3, February.

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