  The proliferation of smart, connected, always listening devices have
introduced significant privacy risks to users in a smart home environment.
Beyond the notable risk of eavesdropping, intruders can adopt machine learning
techniques to infer sensitive information from audio recordings on these
devices, resulting in a new dimension of privacy concerns and attack variables
to smart home users. Techniques such as sound masking and microphone jamming
have been effectively used to prevent eavesdroppers from listening in to
private conversations. In this study, we explore the problem of adversaries
spying on smart home users to infer sensitive information with the aid of
machine learning techniques. We then analyze the role of randomness in the
effectiveness of sound masking for mitigating sensitive information leakage. We
propose a Generative Adversarial Network (GAN) based approach for privacy
preservation in smart homes which generates random noise to distort the
unwanted machine learning-based inference. Our experimental results demonstrate
that GANs can be used to generate more effective sound masking noise signals
which exhibit more randomness and effectively mitigate deep learning-based
inference attacks while preserving the semantics of the audio samples.
