The Internet of Things (IoT) devices have been increasingly deployed in smart homes and smart buildings to monitor and control their environments. The Internet traffic data produced by these IoT devices are collected by Internet Service Providers (ISPs) and IoT device manufacturers, and often shared with third-parties to maintain and enhance user services. Unfortunately, extensive recent research has shown that on-path adversaries can infer and fingerprint users' sensitive privacy information such as occupancy and user in-home activities by analyzing IoT network traffic traces. Most recent approaches that aim at defending against these malicious IoT traffic analytics can not sufficiently protect user privacy with reasonable traffic overhead. In particular, many approaches did not consider practical limitations, e.g., network bandwidth, maximum package injection rate or actual user in-home behavior in their design. To address this problem, we design a new low-cost, open-source user "tunable" defense system---PrivacyGuard that enables users to significantly reduce the private information leaked through IoT device network traffic data, while still permitting sophisticated data analytics or control that is necessary in smart home management. In essence, our approach employs intelligent deep convolutional generative adversarial networks (DCGANs)-based IoT device traffic signature learning, long short-term memory (LSTM)-based artificial traffic signature injection, and partial traffic reshaping to obfuscate private information that can be observed in IoT device traffic traces. We evaluate PrivacyGuard using IoT network traffic traces of 31 IoT devices from 5 smart homes. We find that PrivacyGuard can effectively prevent a wide range of state-of-the-art machine learning-based and deep learning-based occupancy and other 9 user in-home activity detection attacks. We release the source code and datasets of PrivacyGuard to IoT research community.

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