A GAN-based Approach for Mitigating Inference Attacks in Smart Home Environment

TL;DR

This paper introduces a GAN-based method to generate effective sound masking noise, significantly reducing inference attacks on smart home audio devices while maintaining audio semantics.

Contribution

The study proposes a novel GAN-based approach for privacy preservation in smart homes, enhancing sound masking effectiveness against machine learning inference attacks.

Findings

GAN-generated noise improves privacy protection

Sound masking with GANs reduces inference accuracy

Audio semantics are preserved during noise generation

Abstract

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.