Keeping the Smart Home Private with Smart(er) IoT Traffic Shaping
This addresses privacy risks for smart home users from traffic analysis, offering a novel defense against incremental improvements in existing methods.
The paper tackles the problem of inferring private in-home activities from encrypted IoT traffic by a passive observer, and develops a stochastic traffic padding defense that reduces detection accuracy with theoretical bounds on adversary capability.
The proliferation of smart home Internet of Things (IoT) devices presents unprecedented challenges for preserving privacy within the home. In this paper, we demonstrate that a passive network observer (e.g., an Internet service provider) can infer private in-home activities by analyzing Internet traffic from commercially available smart home devices even when the devices use end-to-end transport-layer encryption. We evaluate common approaches for defending against these types of traffic analysis attacks, including firewalls, virtual private networks, and independent link padding, and find that none sufficiently conceal user activities with reasonable data overhead. We develop a new defense, "stochastic traffic padding" (STP), that makes it difficult for a passive network adversary to reliably distinguish genuine user activities from generated traffic patterns designed to look like user interactions. Our analysis provides a theoretical bound on an adversary's ability to accurately detect genuine user activities as a function of the amount of additional cover traffic generated by the defense technique.