Privacy Protection for Audio Sensing Against Multi-Microphone Adversaries

Authors: Chuhan Gao (University of Wisconsin-Madison), Kassem Fawaz (University of Wisconsin-Madison), Sanjib Sur (University of South Carolina), Suman Banerjee (University of Wisconsin-Madison)

Volume: 2019
Issue: 2
Pages: 146–165

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Abstract: Audio-based sensing enables fine-grained human activity detection, such as sensing hand gestures and contact-free estimation of the breathing rate. A passive adversary, equipped with microphones, can leverage the ongoing sensing to infer private information about individuals. Further, with multiple microphones, a beamforming-capable adversary can defeat the previously-proposed privacy protection obfuscation techniques. Such an adversary can isolate the obfuscation signal and cancel it, even when situated behind a wall. AudioSentry is the first to address the privacy problem in audio sensing by protecting the users against a multi-microphone adversary. It utilizes the commodity and audio-capable devices, already available in the user’s environment, to form a distributed obfuscator array. AudioSentry packs a novel technique to carefully generate obfuscation beams in different directions, preventing the multi-microphone adversary from canceling the obfuscation signal. AudioSentry follows by a dynamic channel estimation scheme to preserve authorized sensing under obfuscation. AudioSentry offers the advantages of being practical to deploy and effective against an adversary with a large number of microphones. Our extensive evaluations with commodity devices show that AudioSentry protects the user’s privacy against a 16microphone adversary with only four commodity obfuscators, regardless of the adversary’s position. AudioSentry provides its privacy-preserving features with little overhead on the authorized sensor.

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