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The number and variety of Internet-connected devices have grown enormously inthe past few years, presenting new challenges to security and privacy. Researchhas shown that network adversaries can use traffic rate metadata from consumerIoT devices to infer sensitive user activities. Shaping traffic flows to fitdistributions independent of user activities can protect privacy, but thisapproach has seen little adoption due to required developer effort and overheadbandwidth costs. Here, we present a Python library for IoT developers to easilyintegrate privacy-preserving traffic shaping into their products. The libraryreplaces standard networking functions with versions that automaticallyobfuscate device traffic patterns through a combination of payload padding,fragmentation, and randomized cover traffic. Our library successfully preservesuser privacy and requires approximately 4 KB/s overhead bandwidth for IoTdevices with low send rates or high latency tolerances. This overhead isreasonable given normal Internet speeds in American homes and is an improvementon the bandwidth requirements of existing solutions.

A Developer-Friendly Library for Smart Home IoT Privacy-Preserving Traffic Obfuscation