TrafficDiary: User Attribute Inference Based on Smart Home Traffic Traces

Smart home technology has found wide-ranging applications in daily life, from enhancing energy efficiency to simplifying daily tasks and providing greater convenience. However, recent works have found that smart home devices are vulnerable to passive network observers (i.e., adversaries). While adversaries have demonstrated the ability to infer device events (e.g., whether a lamp is turned on) from the encrypted smart home traffic, we believe this only represents a less critical aspect of smart home privacy risks. Further analysis of demographic attributes presents greater risks to user privacy. Besides, from our deployment experience of real-world smart homes, we found that existing event inference methods can be greatly interfered with by event-unrelated traffic. Experiments show that this interference can result in up to a 10% drop in inference accuracy. Furthermore, it is challenging to infer finer-grained demographic attributes, due to the insufficient accuracy of event inference. Therefore, in this work, we propose a novel event inference model extracting multi-dimensional features that reduces the interference of event-unrelated traffic by analyzing packet length distribution and statistical properties. In addition, we design a dual-channel neural network to extract spatial and temporal relationships among triggered events to infer demographic attributes of smart home users, such as age group and career stage. Combining the above designs, we present TrafficDiary, the first user attribute inference approach based on smart home traffic traces. We prototype TrafficDiary and evaluate it in real-world smart homes. Experimental results show that TrafficDiary achieves 98.68% accuracy with a zero false positive rate in event inference and a high level of accuracy in user attribute inference, even when 16, 362 groups of event-unrelated traffic exist. TrafficDiary also performs well in terms of efficiency, with an inference latency of only 1.82 ms on a Raspberry Pi 4B device.