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- W4385065498 abstract "The proliferation of smart, connected, always-listening devices has introduced significant privacy risks to users in wireless networks comprising dense massive devices. 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 for wireless network users. Techniques such as sound masking and microphone jamming have effectively prevented eavesdroppers from listening to private conversations. In this study, we explore the problem of adversaries spying on wireless network users to infer sensitive information with 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 the network, 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 in wireless networks. The GANs would find useful applications in addressing the proliferating privacy and security concerns in 5G and the envisioned 6G wireless networks." @default.
- W4385065498 created "2023-07-23" @default.
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- W4385065498 date "2023-07-20" @default.
- W4385065498 modified "2023-10-16" @default.
- W4385065498 title "A generative adversarial network-based approach for mitigating inference attacks in emerging wireless networks" @default.
- W4385065498 doi "https://doi.org/10.1049/pbse021e_ch16" @default.
- W4385065498 hasPublicationYear "2023" @default.
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