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

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
Authors to whom correspondence should be addressed.
Academic Editor: Georgios Kambourakis
Future Internet 2017, 9(1), 7;
Received: 23 December 2016 / Revised: 28 February 2017 / Accepted: 1 March 2017 / Published: 5 March 2017
(This article belongs to the Special Issue Security and Privacy in Wireless and Mobile Networks)
PDF [3134 KB, uploaded 5 March 2017]


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. View Full-Text
Keywords: smart home; privacy; FATS attack; supervised learning smart home; privacy; FATS attack; supervised learning

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He, J.; Xiao, Q.; He, P.; Pathan, M.S. An Adaptive Privacy Protection Method for Smart Home Environments Using Supervised Learning. Future Internet 2017, 9, 7.

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