# An Adaptive Privacy Protection Method for Smart Home Environments Using Supervised Learning
## 1 Introduction  
The most profound technologies are those that eventually disappear. They weave themselves into the fabric of every day life, until they become indistinguishable. Wireless sensors are becoming ubiquitous in smart home applications and residential environments. Smart home applications integrate multiple Internet of Things (IoT) devices and services that store, process, and exchange data. Micro-controllers can be used to analyze the status of the sensors for identifying events or the activities of residents. They then respond to these events and activities by controlling certain mechanisms that are built within the home. A simple example of such a smart behavior is to turn on the lights when a person enters a room [1]. This can be realized by triggering an infrared sensor when the person enters the room, and the micro-controller can combine the activity and the brightness of the room, to determine whether the lights should be turned on. There will be a multitude of devices in a wireless sensor network (WSN) around residential environments. These monitoring devices can be classified into three categories: sensors, physiological devices, and multimedia devices. Sensors are used to measure the environmental parameters. Physiological devices monitor health conditions and vital signs. Multimedia devices capture audiovisual information and provide an interface between the system and the user [2]. A sensor in a residence is a simple autonomous host device that can sense a phenomenon, convert signals into data, process the data, and then transmit it to a sink node for further analysis [3]. However,the societal concerns of smart home technology evolution, in relation to the privacy and security of the citizen, appear to be at an embryonic stage [4]. Although smart home technologies can bring a great deal of convenience to residents, it is also possible that people's daily behaviors in such an environment would become exposed to attackers, who can use the smart home in a malicious way. Therefore, the issue of privacy protection in the smart home environment has become one of the most important challenges. In the smart home scenario, almost all of the sensor nodes only transmit information when a related event is detected, which is called event-triggered transmission. A global adversary has the ability to monitor the traffic of the entire sensor network, and thus, can immediately detect the origin and time of event-triggered transmissions. Although encryption algorithms can be used to protect data in the transmission, the emergence of new types of attack methods can make such traditional approaches invalid. Such attacks only need access to the timestamp and fingerprint data of each radio message, and in a wireless environment, the fingerprint is a set of features of a radio frequency waveform that are unique to a particular transmitter. Thus, the primary attacks that we are concerned with here are the Fingerprint And Timing-based Snooping (FATS) attacks [5], which have been shown to be very effective in inferring the Activity of Daily Livings (ADLs) of the residents. The most simple and effective way of resisting FATS attacks is to inject fake messages into the transmission sequence. There have been extensive studies on the protection of the privacy of residents in a smart home environment, by taking into consideration the limitations of communication bandwidth, battery energy, and computing power. Most solutions proposed so far are based on a fixed frequency or probabilistic model, which make it hard to identify the real messages in the sequence of messages, even if the attacker can access the global information. These solutions have a major drawback, however, i.e., the reporting of a real event could be delayed until the next scheduled transmission. The delay of sensed data can cause degradation of the Quality of Service (QoS) in many applications, especially in those with intelligent sensing, where sensor data need to be obtained in real time in order to make decisions.