  Recent years have witnessed the bloom development of the human-centered
wireless sensing applications, in which some human information, such as the
user's identity and motions, can be retrieved through analyzing the signal
distortion caused by the target person. However, the openness of wireless
transmission raises increasing concerns on user privacy, since either the human
identity or human motion is sensitive in certain scenarios, including personal
residence, laboratory, and office. Researchers have reported that commodity
WiFi signals can be abused to identify users. To dispel this threat, in this
paper we propose a privacy-preserving framework to effectively hide the
information of user behaviors in wireless signals while retaining the ability
of user authentication. The core of our framework is a novel Siamese
network-based deep model, namely RFBP-Net. In this way, wireless sensing
reveals user information moderately. We conduct extensive experiments on both
the real WiFi and RFID system and open datasets. The experiment results show
that RFBP-Net is able to significantly reduce the activity recognition
accuracy, i.e., 70% reduction in the RFID system and 80% reduction in the WiFi
system, with a slight penalty in the user authentication accuracy, i.e., only
5% and 1% decrease in the RFID and WiFi system, respectively.
