Packet Padding for Improving Privacy in Consumer IoT

Traffic analysis of side-channel information represents a major concern in consumer IoT privacy, since it can lead to a leakage of user data and behavior. In this paper, we propose a lightweight padding-based mechanism to obfuscate one type of side-channel information, the packet length, in order to enhance user privacy. A prototype of the mechanism is implemented in a virtual machine along with five different machine learning classifiers, which are used to identify IoT devices by means of packet length patterns. Results show that the packet padding mechanism is able to reduce the accuracy of the classifiers in at least 75%, thus enhancing the privacy of the IoT communication. Additionally, the delay, jitter, throughput and packet loss are used to quantify the impact of the mechanism on the communication performance of the IoT devices. It is shown that the communication overhead generated the by proposed mechanism was kept to a minimum.