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

Jingsha He, Qi Xiao, Peng He and Muhammad Salman Pathan
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Jingsha He: Faculty of Information Technology & Beijing Engineering Research Center for IoT Software and Systems, Beijing University of Technology, Beijing 100124, 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

Future Internet, 2017, vol. 9, issue 1, 1-15

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.

Keywords: smart home; privacy; FATS attack; supervised learning (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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