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.

中文翻译：

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在智能家居环境中减轻推理攻击的基于 GAN 的方法

智能、互联、始终聆听设备的激增给智能家居环境中的用户带来了重大的隐私风险。除了窃听的显着风险之外，入侵者还可以采用机器学习技术从这些设备上的录音中推断出敏感信息，从而为智能家居用户带来新的隐私问题和攻击变量。诸如声音掩蔽和麦克风干扰等技术已被有效地用于防止窃听者窃听私人谈话。在这项研究中，我们探索了对手在机器学习技术的帮助下监视智能家居用户以推断敏感信息的问题。然后，我们分析随机性在声音掩蔽减轻敏感信息泄漏的有效性中的作用。我们提出了一种基于生成对抗网络 (GAN) 的智能家居隐私保护方法，该方法会产生随机噪声来扭曲不需要的基于机器学习的推理。我们的实验结果表明，GAN 可用于生成更有效的声音掩蔽噪声信号，这些信号表现出更多的随机性，并有效减轻基于深度学习的推理攻击，同时保留音频样本的语义。